BCI Robotics

BCI is a direct communication pathway between the brain and external devices. Electroencephalographic (EEG) based Brain Computer Interface (BCI) equipment usage are getting popular in recent researches. The brain waves (Alfa, Beta, Delta, Gama, and Theta) are applied to different areas such as attention loss, treatment of cancer and the equipment for facilitating the life quality of disabled people that leads to new approaches. In this project, we developed a visual BCI by using Neurosky Mindwave and a mechanical system is controlled with eyes movements.

Brain-computer interface (BCI) or brain-machine interface (BMI) is a way to communicate between the brain and hardware without the use of muscles or the peripheral nervous system. Simultaneous EEG+fNIRS metric have recently been developed. Until now, few researchers have analysed the possibilities of incorporating hemodynamic responses to their electrophysiologic counterparts in a hybrid way to increase overall performance of the system and accuracy. The combination of fNIRS and EEG signals provide a hybrid sensory motor rhythm based on a brain computer interface (BCI) paradigm. A meta-classifier has been designed to integrate the output probability of each classifier modality. The study found that the simultaneous EEG+fNIRS measurement can substantially improve classification precision.

Auditory brain-computer interfaces (BCIs) serve as an access pathway for individuals who present as locked-in and have concomitant visual impairments, but they do not perform as well as visual BCIs. Since hybrid BCIs that combine electroencephalography (EEG) and nearinfrared spectroscopy (NIRS) outperform unimodal BCIs in motor imagery tasks, we hypothesized that such a hybrid BCI would outperform a unimodal BCI in an auditory oddball task. Eleven non-disabled adults (aged 25.6 ± 3.3) participated in two offline sessions and eight returned for a third online session. While combined EEG-NIRS classification did not outperform EEG-exclusive classification, combined classification does show modest gains in 2 participants when using all the NIRS data but constraining the EEG data to only a single oddball. In conclusion, NIRS offers limited benefits in terms of classification in a prolonged auditory task paradigm, but future research should focus on optimizing task duration for EEG and NIRS classification. iii Dedication To my parents for always encouraging me to pursue my dreams.

Brain-computer interface (BCI) systems based on functional near-infrared spectroscopy (fNIRS) have been used as a way of facilitating communication between the brain and peripheral devices. The BCI provides an option to improve the walking pattern of people with poor walking dysfunction, by applying a rehabilitation process. A state-of-the-art step-wise BCI system includes data acquisition, pre-processing, channel selection, feature extraction, and classification. In fNIRS-based BCI (fNIRS-BCI), channel selection plays a vital role in enhancing the classification accuracy of the BCI problem. In this study, the concentration of blood oxygenation (HbO) in a resting state and in a walking state was used to decode the walking activity and the resting state of the subject, using channel selection by Least Absolute Shrinkage and Selection Operator (LASSO) homotopy-based sparse representation classification. The fNIRS signals of nine subjects were collected from the left hemisphere of the ...

Brain Computing interface technology represents a very highly growing field now-a-days for the research because of its unique applications system. In this paper we investigate classification methods of mental commands based on EEG data for BCI. The aim of this study is to present the work of training an artificial neural network (ANN) and Neuro-Fuzzy system provided with the data of a few healthy people who are in different mental states thinking about different activities like eating, walking or sleeping etc. It creates a mutual understanding between mind wave signals and the machine or surrounding system. In recent years' applications based on the brain computing interface have gained a rather huge popularity because of its benefits like providing disabled people with a communication and control environment along with different types of movement restorations. It is proving to be revolutionary in the fields of medical and robotics, mind reading and remote communication etc. Thi...

All rights reserved Near infrared (NIR) spectroscopy can optically measure the concentrations of oxygenated and deoxygenated cerebral haemoglobin (henceforth abbreviated as HbO and Hb, respectively). An NIR device typically consists of an array of near infrared light sources and detectors. As light enters the cerebral cortex, it interacts with the constituents of the tissues and is absorbed or scattered. 1 The transflected light that reaches the detector can then be recorded. The concentrations of HbO and Hb can then be calculated from the intensity of the attenuated light using the modified Beer-Lambert law. 2 Concentrations of HbO and Hb in the cerebral cortex change as a result of certain cognitive tasks, such as mental arithmetic or "fast singing" (mentally singing a song at a fast pace). 3 These cognitive tasks elicit neuronal activity in the prefrontal cortex such that oxygen is consumed and subsequently replenished, leading to its saturation. 2 There is a known time delay of about 2-8 s between the onset of the activation task

The trends in movement-related functional activity measurement for brain-computer interface (BCI) are mostly associated with the central lobe of the brain. This consideration may be a faulty approach for the paralyzed patient. This limitation demands an alternative approach for movement-related BCI. For the first time, we propose the prefrontal hemodynamics for implementing movement-related BCI. This paper aims to model the activation pattern and the classification performances of the prefrontal hemodynamics regarding the movement-related events. Utilizing functional near-infrared spectroscopy (fNIRS) the changes in the concentration of the oxidized hemoglobin and deoxidized hemoglobin regarding voluntary and imagery movements are acquired. With necessary preprocessing, the fNIRS signals are statistically analyzed to localize the most significant activated regions regarding the applied stimuli. The experiment shows that movement-related events have a strong correlation with the prefrontal hemodynamics. The patterns of the movement-related hemodynamics are modeled by polynomial regression and the patterns are used to classify the voluntary and imagery events based on the maximum similarity approach. The resulting classification accuracies are found promising that proves the effectiveness of the prefrontal fNIRS signal to be effective in movement-related brain functionality analysis.

We Shows a novel virtual fitting room framework using a depth sensor, which provides a realistic fitting experience with customized motion filters, size adjustments and physical simulation. The proposed scaling method adjusts the avatar and calculate a standardized apparel size according to the user's measurements, prepares the collision mesh and the physics simulation, with a total of 1 s preprocessing time. The real-time motion filters prevent unnatural artifacts due to the bug from depth sensor or self barrier body parts. We apply bone splitting to realistically render the body parts near the joints. All components are combined efficiently to keep the frame rate higher than previous works while not sacrificing realism.

Most of the traditional works on emotion recognition utilize manifestation of emotion in face, voice, gesture/posture and bio-potential signals of the subjects. However, these modalities of emotion recognition cannot totally justify its significance because of wide variations in these parameters due to habitat and culture. The paper aims at recognizing emotion of people directly from their brain response to infrared signal using music as the stimulus. A type-2 fuzzy classifier has been used to eliminate the effect of intra and inter-personal variations in the featurespace, extracted from the infrared response of the brain. A comparative analysis reveals that the proposed interval type-2 fuzzy classifier outperforms its competitors by classification accuracy as the metric.

This work investigates the classification of finger-tapping task images constructed for the initial dip duration of hemodynamics (HR) associated with the small brain area of the left motor cortex using functional near-infrared spectroscopy (fNIRS). Different layers (i.e., 16-layers, 19-layers, 22-layers, and 25-layers) of isolated convolutional neural network (CNN) designed from scratch are tested to classify the right-hand thumb and little finger-tapping tasks. Functional t-maps of finger-tapping tasks (thumb, little) were constructed for various durations (0.5 to 4 s with a uniform interval of 0.5 s) for the initial dip duration using a three gamma functions-based designed HR function. The results show that the 22-layered isolated CNN model yielded the highest classification accuracy of 89.2% with less complexity in classifying the functional t-maps of thumb and little fingers associated with the same small brain area using the initial dip. The results further demonstrated that th...

In this paper we demonstrate the coupling of an autonomous planning and control framework for whole-body humanoid motion, with a brain-computer interface (BCI) system in order to achieve online realtime biasing and correction of the offline planned motion. Using the contact-before-motion planning paradigm, the humanoid autonomously plans, in a first stage, its motion to reach a desired goal configuration or contact location. In the second stage of the approach, the humanoid executes the planned motion and the user can exert online some control on the motion being executed through an EEG decoding interface. The method is applied and demonstrated in a dynamics simulator with full collision-detection on a model of the humanoid robot HRP2.

Brain Computer Interfaces (BCI) has become a vital field of biomedical engineering
and computation, which uses electrical signals obtained from electroencephalogram
exams (EEG) to provide assistive technologies (AT) for humans. The goal of BCI is to
interpret brain activity in digital format and act with commands on electromechanical
devices, so that people with limitations to produce a motor response can
communicate with a computer device and achieve greater self-sufficiency, social
inclusion and freedom. One of the main challenges in current BCI research is
extracting features from time-varying random EEG signals and classifying them as
accurately as possible. Feature extraction techniques are used to extract features
that represent a unique property obtained from the brain signal pattern. Thus, this
article presents the results of analyzing specific movement intent of EEG signals to
extract the appropriate features using statistical classification techniques and
algorithms, which can be employed to control BCI devices, so they can be used by
people with temporary disabilities, disabled or paralyzed.
The EEG characteristics in terms of power spectral density (PSD), spectral centroids,
standard deviation and entropy techniques were selected and investigated from two
different mental exercises; The selected signals are classified using Linear
Discriminant Analysis (LDA), Common Spatial Patterns (CSP). The best accuracy
was achieved by the power spectral density. The accuracies of this feature are 75%
to 99%, depending on the quantity and quality of the data samples collected. Finally,
the translation algorithm will be built using selected and classified EEG resources to
control the ICM devices.

Keywords: Brain Computer Interface. Neural networks. Electroencephalogram
Signs. Machine Learning;

Brain-Computer Interface (BCI) is a promising technology that enables people affected by neuromuscular disorders to control external devices like a wheelchair or prosthesis with the help of their brain signals. Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical neuroimaging technique that describes the brain hemodynamics. Recently, it has been investigated for the development of BCI application. In the current study, fNIRS signals that report changes in hemoglobin concentrations acquired during imagination of executing four motor tasks namely, right-fist clenching, left-fist clenching, right-and left-foot tapping and rest have been explored. Besides employing conventional machine learning algorithms used in BCI field such as Support Vector Machine (SVM), Multilayer Perceptron (MLP) neural network, and Projection-based learning in a Meta-cognitive Radial Basis Function Network (PBL-McRBFN), a deep learning approach namely, Convolutional Neural Network (CNN) has been explored to classify the motor imagery fNIRS signals. Also, the selection of the best input image representation of the one-dimensional fNIRS signal to be used for CNN architecture is investigated. In comparison to conventional machine learning algorithms, the performance of CNN was superior with respect to the classification of four-class motor imagery fNIRS signals, with an average classification accuracy value of 72.35 ± 4.4%. The results show that the classification of fNIRS signals is possible using deep learning approach for the development of BCI application specifically using spectrogram representation of the fNIRS signal.

Brain-computer interfaces (BCIs) allow individuals to use only cognitive activities to interact with their environment. The widespread use of BCIs is limited, due in part to their lack of user-friendliness. The main goal of this work was to develop a more user-centered BCI and determine if: (1) individuals can acquire control of an online near-infrared spectroscopy BCI via usability and performance-informed selection of mental tasks without compromising classification accuracy and (2) the combination of usability and performance-informed selection of mental tasks yields subjective ease-of-use ratings that exceed those attainable with prescribed mental tasks. Twenty able-bodied participants were recruited. Half of the participants served as a control group, using the state-of-the-art prescribed mental strategies. The other half of the participants comprised the study group, choosing their own personalized mental strategies out of eleven possible tasks. It was concluded that users were, in fact, able to acquire control of the more user-centered BCI without a significant change in accuracy compared to the prescribed task BCI. Furthermore, the personalized BCI yielded higher subjective ease-of-use ratings than the prescribed BCI. Average online accuracies of 77 AE 12.9% and 73 AE 12.9% were achieved by the personalized and prescribed mental task groups, respectively.

Brain-Computer Interface (BCI) machines are capable of obtaining brain activities by conducting Electroencephalogram tests. Developments on both BCI and Machine Learning allowed various researchers to develop and study various BCI control systems, mainly varying with the algorithm implementation. This research presents a performance analysis of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for BCI control systems for drone maneuverability. Eye gestures were used to generate the EEG data that were captured using the Emotiv INSIGHT Neuroheadset. The obtained data were transferred to the computing hardware using IEEE 802.15 wirless communication protocol (i.e. Bluetooth connectivity); the data are processed using the 5th order Butterworth Band-Pass filtering and heuristic filtering. The filtered dataset is then fed to the ANFIS and a Support Vector Machine (SVM) algorithm, the latter serving as the basis, for training and quadcopter control implementation. Three flight tests were d...

This paper presents the conceptual framework for a study of musical experience and the associated architecture centred on Human-Humanoid Interaction (HHI). On the grounds of the theoretical and experimental literature on the biological foundation of music, the grammar of music perception and the perception and feeling of emotions in music hearing, we argue that music cognition is specific and that it is realized by a cognitive capacity for music that consists of conceptual and affective constituents. We discuss the relationship between such constituents that enables understanding, that is extracting meaning from music at the different levels of the organization of sounds that are felt as bearers of affects and emotions. To account for the way such cognitive mechanisms are realized in music hearing and extended to movements and gestures we bring in the construct of tensions and of music experience as a cognitive frame. Finally, we describe the principled approach to the design and the architecture of a BCI-controlled robotic system that can be employed to map and specify the constituents of the cognitive capacity for music as well as to simulate their contribution to music meaning understanding in the context of music experience by displaying it through the Geminoid robot movements.

The study and analysis of the electrical activity of the brain is valuable in understanding the human mental state, intentions and will. This aids the development of Brain Computer Interface (BCI), facilitating communication between the human brain and computer, by converting the brain waves (EEG waves-Electroencephalography) into control signals. These control signals can then be used to trigger an external device, thereby enabling a seamless communication with the intelligent system. It unfolds various avenues for research and further applications in the realm of prosthetic device control, development of thought controlled intelligent systems and other complex interfaces. This will also be an aid to persons with disabilities or various other amputations. In this work, a novel feature extraction algorithm is proposed for extracting event related potentials from the EEG signals using wavelet sub band entropy which can be used in BCI applications. An attention index is defined which gives a measure of the amount of concentration or attention the subject has, upon focussing on a particular event or thought.

Artificial limbs and exoskeletons have been widely used in a variety of applications, from military to medicine. The Defence Advanced Research Projects Agency (DARPA) primarily focuses on developing exoskeletons to aid ground soldiers in both physical performance skills and survivability. Robotic arms have been used to assist personalities who have lost the ability to perform everyday activities such as walking because of grievous medical injuries.The main objective of this project is to control a Robotic arm with the power of one's brain. The Brainwave regulator system will use brain waves to initiate, stop and change whereabouts of robotic arm. The main component of the control system is the Emotiv EPOC sensor, which detects the brain functions like thoughts. With the advancements in technology and healthcare facilities, the number of senior citizens and elderly who find it difficult to walk are benefitted. Hence there is a need for designing a robotic arm that is user friendly and this involves fewer complexities. The arm can also be used by the physically challenged who depend on others for physical movements that are challenging. Rehabilitation centres at hospitals can make use of this robotic arm.In this project we acquire the signal using the emotiv epoc. The acquired signals are processed using the LabVIEW environment. The various thought based signals are obtained, based on which the arm is activated.

Brain-Computer Interface can be non-invasive devices that obtain signals generated from the brain and are then manipulated to suit various applications. A popular application for BCI is interfacing with robotics; and, each BCI-Robotics system employed different Machine Learning algorithms. This study aimed to present a performance analysis for a Neuro-Fuzzy algorithm, specifically the Adaptive-Network-Fuzzy-Inference System (ANFIS), to classify EEG signals retrieved by the Emotiv INSIGHT. An SVM algorithm is also developed to serve as a reference vs the ANFIS's performance. A methodology for generation and acquisition of EEG signals can be used by researchers as reference. Facial and Eye Gestures were utilized as means of EEG signal generation which are fed to both algorithms for simulation experiments. Results showed that the ANFIS tend to be more reliable and marginally better than of the SVM algorithm. Compared to SVM, the ANFIS took significant amounts of computational resources requiring higher specs and training time.

Brain-Computer Interface (BCI) machines are capable of obtaining brain activities by conducting Electroencephalogram tests. Developments on both BCI and Machine Learning allowed various researchers to develop and study various BCI control systems, mainly varying with the algorithm implementation. This research presents a performance analysis of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for BCI control systems for drone maneuverability. Eye gestures were used to generate the EEG data that were captured using the Emotiv INSIGHT Neuroheadset. The obtained data were transferred to the computing hardware using IEEE 802.15 wireless communication protocol (i.e. Bluetooth connectivity); the data are processed using the 5 th order Butterworth Band-Pass filtering and heuristic filtering. The filtered dataset is then fed to the ANFIS and a Support Vector Machine (SVM) algorithm, the latter serving as the basis, for training and quadcopter control implementation. Three flight tests were done, hover test, flight command test, and the flight control test to obtain the performance of the control system in terms of accuracy. Results from the initial two tests showed that the ANFIS performed comparably with the SVM, and even about 2% better with an accuracy of 79%. The final test showed that the BCI control system had a maximum variance of 4% compared to the handheld remote controller, where the latter served as the basis. It was found that between Machine Learning algorithms, ANFIS is as capable as the SVM for BCI control systems. Further developments may focus on employing time-series EEG preprocessing techniques.

This paper presents the conceptual framework for a study of musical experience and the associated architecture centred on Human-Humanoid Interaction (HHI). On the grounds of the theoretical and experimental literature on the biological foundation of music, the grammar of music perception and the perception and feeling of emotions in music hearing, we argue that music cognition is specific and that it is realized by a cognitive capacity for music that consists of conceptual and affective constituents. We discuss the relationship between such constituents that enables understanding, that is extracting meaning from music at the different levels of the organization of sounds that are felt as bearers of affects and emotions. To account for the way such cognitive mechanisms are realized in music hearing and extended to movements and gestures we bring in the construct of tensions and of music experience as a cognitive frame. Finally, we describe the principled approach to the design and the architecture of a BCI-controlled robotic system that can be employed to map and specify the constituents of the cognitive capacity for music as well as to simulate their contribution to music meaning understanding in the context of music experience by displaying it through the Geminoid robot movements.

Classification of electroencephalogram (EEG) signal is important in mental decoding for brain-computer interfaces (BCI). We introduced a feature extraction approach based on frequency domain analysis to improve the classification performance on different mental tasks using single-channel EEG. This biologically inspired method extracts the most discriminative spectral features from power spectral densities (PSDs) of the EEG signals. We applied our method on a dataset of six subjects who performed five different imagination tasks: (i) resting state, (ii) mental arithmetic, (iii) imagination of left hand movement, (iv) imagination of right hand movement, and (v) imagination of letter "A." Pairwise and multiclass classifications were performed in single EEG channel using Linear Discriminant Analysis and Support Vector Machines. Our method produced results (mean classification accuracy of 83.06% for binary classification and 91.85% for multiclassification) that are on par with ...

In this paper, a new adaptive neural network classifier (ANNC) of EEG-P300 signals from mental activities is proposed. To overcome an overtraining of the classifier caused by noisy and non-stationary data, the EEG signals are filtered and their autoregressive (AR) properties are extracted using an AR model before being passed to the ANNC. For evaluation purposes, the same data in Hoffmann et al. (2008) were used. With and without the AR property extraction, the proposed ANNC could achieve 100% accuracy for all the subjects. To verify the performance improvement of the proposed classification scheme, a comparison of the ANNC and the conventional back-propagation neural network classifier was performed as well.

The dwindling financial performance and the constant pressure to reduce cost and human intervention from boring tasks and repetitive led to the complexity of software programming languages blurring the lines between digital, physical and biological spheres. As a result of solving the artificial intelligence problem resulted in technological singularity of making humans to remain irrelevant. The event of Covid-19 variants rapidly contributed to the globalization of superintelligence. To deal with the AI apocalypseis to ensure that the advent of digital super intelligence is symbiotic with humanity by increasing the brain bandwidth to make humans be at par with broadening AI’s scope. This study employed survey research design. A self-administered questionnaire was distributed to 298 respondents. Descriptive and inferential statistics (linear regression) were employed in testing the stated hypothesis. The study discovered that there exists a positive relationship between all of the ind...

This paper considers the problem of collision-free navigation of omnidirectional mobile robots in environments with obstacles. Information from a monocular camera, encoders, and an inertial measurement unit is used to achieve the task. Three different visual servoing control schemes, compatible with the class of considered robot kinematics and sensor equipment, are analysed and their robustness properties with respect to actuation inaccuracies discussed. Then, a controller is proposed with formal guarantee of convergence to the bisector of a corridor. The main controller components are a visual servoing control scheme and a velocity estimation algorithm integrating visual, kinematic and inertial information. The behaviour of all the considered algorithms is analised and illustrated through simulations both for a wheeled and a humanoid robot. The solution proposed as the most efficient and robust with respect to actuation inaccuracies is also validated experimentally on a real humanoid NAO.

The main notion of this paper is to identify the cognitive load during a mental arithmetic task experiment using fNIRS signals. The first objective is to classify the difficulty level and the state of inactivity during the given task. To identify the classes, the feature vectors have to undergo all the possible steps of a pattern classification problem. In this paper, we have developed a novel Feature Selection technique to reduce the dimension of the feature vectors by omitting the redundant features. For this purpose, an objective function depending upon the class density or likelihood functions is optimized using the well-known Differential Evolution algorithm. General type-2 fuzzy classifier is used for subsequent classification step. The proposed Feature selection technique gives a satisfactory accuracy results over principal component analysis. Also the fuzzy classifier outperforms the other well-known classifier like support vector machine, k-nearest neighborhood. The load of a subject undergoing the experiment is measured at a particular class relying upon the mean type-1 fuzzy value of all feature entities.

Today, millions of peoples are suffering due to the lack of a functional arm preventing from doing things. In Sri Lanka there are about over thousands of people are suffering from disabilities. Sri Lanka Ministry of Health state that the number of disabled persons in Sri Lanka will be increased by 24.2% by 2025. Most of them will suffer from hand disabilities. Disabling a hand costs huge disadvantages to a human being. Living without a hand will be a major problem for those who having hand disabilities. The aim of this project is to give a solution to those who suffer from hand disabilities and make their life efficient and easy. The biosignal-based controlling system is the next step in order to achieve more accuracy. Bio-signals are referring as the Electroencephalography (EEG), Electromyogram (EMG) and Electrocardiogram (ECG) signals. The robot hand motions and movements of the fingers will be completely dependent on the brainwaves which human beings will produce. This project ma...

Stroke and neurodegenerative diseases, among a range of other neurologic disorders, can cause chronic paralysis. Patients suffering from paralysis may remain unable to achieve even basic everyday tasks such as liquid intake. Currently, there is a great interest in developing robotic assistants controlled via brain-machine interfaces (BMIs) to restore the ability to perform such tasks. This paper describes an autonomous robotic assistant for liquid intake. The components of the system include autonomous online detection both of the cup to be grasped and of the mouth of the user. It plans motions of the robot arm under the constraints that the cup stays upright while moving towards the mouth and that the cup stays in direct contact with the user's mouth while the robot tilts it during the drinking phase. To achieve this, our system also includes a technique for online estimation of the location of the user's mouth even under partial occlusions by the cup or robot arm. We tested our system in a real environment and in a sharedcontrol setting using frequency-specific modulations recorded by electroencephalography (EEG) from the brain of the user. Our experiments demonstrate that our BMI-controlled robotic system enables a reliable liquid intake. We believe that our approach can easily be extended to other useful tasks including food intake and object manipulation.

The assistive, adaptive, and rehabilitative applications of EEG-based robot control and navigation are undergoing a major transformation in dimension as well as scope. Under the background of artificial intelligence, medical and nonmedical robots have rapidly developed and have gradually been applied to enhance the quality of people’s lives. We focus on connecting the brain with a mobile home robot by translating brain signals to computer commands to build a brain-computer interface that may offer the promise of greatly enhancing the quality of life of disabled and able-bodied people by considerably improving their autonomy, mobility, and abilities. Several types of robots have been controlled using BCI systems to complete real-time simple and/or complicated tasks with high performances. In this paper, a new EEG-based intelligent teleoperation system was designed for a mobile wall-crawling cleaning robot. This robot uses crawler type instead of the traditional wheel type to be used ...

The study and analysis of the electrical activity of the brain is valuable in understanding the human mental state, intentions and will. This aids the development of Brain Computer Interface (BCI), facilitating communication between the human brain and computer, by converting the brain waves (EEG waves-Electroencephalography) into control signals. These control signals can then be used to trigger an external device, thereby enabling a seamless communication with the intelligent system. It unfolds various avenues for research and further applications in the realm of prosthetic device control, development of thought controlled intelligent systems and other complex interfaces. This will also be an aid to persons with disabilities or various other amputations. In this work, a novel feature extraction algorithm is proposed for extracting event related potentials from the EEG signals using wavelet sub band entropy which can be used in BCI applications. An attention index is defined which gives a measure of the amount of concentration or attention the subject has, upon focussing on a particular event or thought.

This paper presents the conceptual framework for a study of musical experience and the associated architecture centred on Human-Humanoid Interaction (HHI). On the grounds of the theoretical and experimental literature on the biological foundation of music, the grammar of music perception and the perception and feeling of emotions in music hearing, we argue that music cognition is specific and that it is realized by a cognitive capacity for music that consists of conceptual and affective constituents. We discuss the relationship between such constituents that enables understanding, that is extracting meaning from music at the different levels of the organization of sounds that are felt as bearers of affects and emotions. To account for the way such cognitive mechanisms are realized in music hearing and extended to movements and gestures we bring in the construct of tensions and of music experience as a cognitive frame. Finally, we describe the principled approach to the design and th...

A Virtual Trial Room application utilizing Augmented Reality which enables a client to undertake on virtual garments. The client posture and profundity is followed utilizing the Microsoft Kinect sensor and virtual garments are lined up with the followed client. the clothes moves and adjusts practically and therefore the lighting force of the fabric render is adjusted to coordinate surrounding lighting conditions. The exhibited application enhances related increased reality application by including full client posture following and by utilizing 3D dress models joined with fabric recreation instead of 2D pictures. IndexTerms Augmented reality, Microsoft Kinect sensor, 3D clothes.

Functional near-infrared spectroscopy (fNIRS) is a comparatively new noninvasive, portable, and easy-to-use brain imaging modality. However, complicated dexterous tasks such as individual finger-tapping, particularly using one hand, have been not investigated using fNIRS technology. Twenty-four healthy volunteers participated in the individual finger-tapping experiment. Data were acquired from the motor cortex using sixteen sources and sixteen detectors. In this preliminary study, we applied standard fNIRS data processing pipeline, i.e., optical densities conversation, signal processing, feature extraction, and classification algorithm implementation. Physiological and non-physiological noise is removed using 4th order band-pass Butter-worth and 3rd order Savitzky–Golay filters. Eight spatial statistical features were selected: signal-mean, peak, minimum, Skewness, Kurtosis, variance, median, and peak-to-peak form data of oxygenated haemoglobin changes. Sophisticated machine learnin...

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Many people suffer from movement disabilities and would benefit from an assistive mobility device with practical control. This paper demonstrates a face-machine interface system that uses motion artifacts from electroencephalogram (EEG) signals for mobility enhancement in people with quadriplegia. We employed an Emotiv EPOC X neuroheadset to acquire EEG signals. With the proposed system, we verified the preprocessing approach, feature extraction algorithms, and control modalities. Incorporating eye winks and jaw movements, an average accuracy of 96.9% across four commands was achieved. Moreover, the online control results of a simulated power wheelchair showed high efficiency based on the time condition. The combination of winking and jaw chewing results in a steering time on the same order of magnitude as that of joystickbased control, but still about twice as long. We will further improve the efficiency and implement the proposed face-machine interface system for a real-power whee...

P300 evoked potential is an electroencephalographic (EEG) signal obtained at the central-parietal region of the brain in response to rare or unexpected events. In this work, an experiment on the detection of a P-300 rhythm for potential applications on brain computer interfaces (BCI) using an Adaptive Neuro Fuzzy algorithm (ANFIS) is presented. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected using the discrete wavelet transform (DWT) applied on the preprocessed signal as a feature extractor, and further entered to the ANFIS system. Experimental results are presented.

In this paper is illustrated the cognitive architecture of a humanoid robot based on the proposed paradigm of Latent Semantic Analysis (LSA). This paradigm is a step towards the simulation of an emotional behavior of a robot interacting with humans. The LSA approach allows the creation and the use of a data driven high-dimensional conceptual space. We developed an architecture based on three main areas: Sub-conceptual, Emotional and Behavioral. The first area analyzes perceptual data coming from the sensors. The second area builds the sub-symbolic representation of emotions in a conceptual space of emotional states. The last area triggers a latent semantic behavior which is related to the humanoid emotional state. The robot shows its overall behavior also taking into account its "personality". We implemented the system on a Aldebaran NAO humanoid robot and we tested the emotional interaction with humans through the use of a mobile phone as an interface.

Nowadays the UX design become on a next level. Together with new way of interaction are introduced as finger and hand movement. The technology offer and thought-driven approach with so called brain-computer interface (BCI). This possibility opens new challenges for uses as well as for designers and researchers. More than 15 years there are devices for brain signal interception, such as EMotiv Epoc, Neurosky headset and others. The reliable translation of user commands to the app on a global scale, with no leaps in advancement for its lifetime, is a challenge. It is still un-solve for modern scientists and software developers. Success requires the effective interaction of many adaptive controllers: the user's brain, which produces brain activity that encodes intent; the BCI system, which translates that activity into the digital signals; the accuracy of aforementioned system, computer algorithms to translate the brain signals to commands. In order to find out this complex and monumental task, many teams are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. Rarely there are publications, in which are described the used methods, steps and algorithms for discerning varying commands, words, signals and etc. This article describes one approach to the retrieval, analysis and processing of the received signals. These data are the result of researching the capabilities of Arduino robot management through the brain signals received by BCI.

Ota et al. PFC Neurofeedback Improves Hand Dexterity performance improvement rates in the motor rehabilitation task. The results suggest that the aPFC might shape activity in the somatosensory motor-related areas to improve hand dexterity. These findings further suggest that the motor imagery training using neurofeedback signals from the aPFC might be useful to patients with motor disability.

A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis (ICA), multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine (SVM), hidden Markov model (HMM), artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming and artificial intelligence. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and nonlinear brain dynamics and related feature extraction and classification challenges. Psychoneurophysiological phenomena and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes progress in BCI field and highlights critical challenges. INDEX TERMS brain computer interface (BCI), hybrid/multimodal BCI, neuroimaging techniques, neurosensors, electrical/hemodynamic brain signals, cognitive rehabilitation

pierre(dot)gergondet(at)aist.go.jp Summary. In this paper, we present our approach to design a brain-computer interface (BCI) that allows the user to perform multitask humanoid control. We efficiently integrate techniques from computer vision and the task-function based control together with the brain-computer interface into an immersive and intuitive control application despite the well-known shortcomings of BCI. This approach is assessed in a user experiment involving 4 subjects who successfully controlled the HRP-2 humanoid robot in a scenario involving both grasping tasks and steering. The user experiences and the interface performances are presented and give a rich insight into future research that can be made to improve and extend such interface.

Despite motion artifacts are a major source of noise in fNIRS infant data, how to approach motion correction in this population has only recently started to be investigated. Homer2 offers a wide range of motion correction methods and previous work on simulated and adult data suggested the use of Spline interpolation and Wavelet filtering as optimal methods for the recovery of trials affected by motion. However, motion artifacts in infant data differ from those in adults' both in amplitude and frequency of occurrence. Therefore, artifact correction recommendations derived from adult data might not be optimal for infant data. We hypothesized that the combined use of Spline and Wavelet would outperform their individual use on data with complex profiles of motion artifacts. To demonstrate this, we first compared, on infant semi-simulated data, the performance of several motion correction techniques on their own and of the novel combined approach; then, we investigated the performance of Spline and Wavelet alone and in combination on real cognitive data from three datasets collected with infants of different ages (5, 7 and 10 months), with different tasks (auditory, visual and tactile) and with different NIRS systems. To quantitatively estimate and compare the efficacy of these techniques, we adopted four metrics: hemodynamic response recovery error, within-subject standard deviation, between-subjects standard deviation and number of trials that survived each correction method. Our results demonstrated that (i) it is always better correcting for motion artifacts than rejecting the corrupted trials; (ii) Wavelet filtering on its own and in combination with Spline interpolation seems to be the most effective approach in reducing the between-and the within-subject standard deviations. Importantly, the combination of Spline and Wavelet was the approach providing the best performance in semi-simulation both at low and high levels of noise, also recovering most of the trials affected by motion artifacts across all datasets, a crucial result when working with infant data.

We report results of a study that utilizes a BCI to drive an interactive interface countermeasure that allows users to self-regulate sustained attention while performing an ecologically valid, long-duration business logistics task. An engagement index derived from EEG signals was used to drive the BCI while fNIRS measured hemodynamic activity for the duration of the task. Participants (n = 30) were split into three groups (1) no countermeasures (NOCM), (2) continuous countermeasures (CCM), and (3) event synchronized, level-dependent countermeasures (ECM). We hypothesized that the ability to self-regulate sustained attention through a neurofeedback mechanism would result in greater task engagement, decreased error rate and improved task performance. Data were analyzed by wavelet coherence analysis, statistical analysis, performance metrics and self-assessed cognitive workload via RAW-TLX. We found that when the BCI was used to deliver continuous interface countermeasures (CCM), task performance was moderately enhanced in terms of total 14,785 (σ = 423) and estimated missed sales 7.46% (σ = 1.76) when compared to the NOCM 14,529 (σ = 510), 9.79% (σ = 2.75), and the ECM 14,180 (σ = 875), 9.62% (σ = 4.91) groups. An "actions per minute" (APM) metric was used to determine interface interaction activity which showed that overall the CCM and ECM groups had a higher APM of 3.460 (SE = 0.140) and 3.317 (SE = 0.139) respectively when compared with the NOCM group 2.65 (SE = 0.097). Statistical analysis showed a significant difference between ECM-NOCM and CCM-NOCM (p < 0.001) groups, but no significant difference between the ECM-CCM groups. Analysis of the RAW-TLX scores showed that the CCM group had lowest total score 7.27 (σ = 3.1) when compared with the ECM 9.7 (σ = 3.3) and NOCM 9.2 (σ = 3.4) groups. No statistical difference was found between the RAW-TLX or the subscales, except for self-perceived performance (p < 0.028) comparing the CCM and ECM groups. The results suggest that providing a means to self-regulate sustained attention has the potential to keep operators engaged over long periods, and moderately increase on-task performance while decreasing on-task error.

Neurofeedback is a promising tool for brain rehabilitation and peak performance training. Neurofeedback approaches usually rely on a single brain imaging modality such as EEG or fMRI. Combining these modalities for neurofeedback training could allow to provide richer information to the subject and could thus enable him/her to achieve faster and more specific self-regulation. Yet unimodal and multimodal neurofeedback have never been compared before. In the present work, we introduce a simultaneous EEG-fMRI experimental protocol in which participants performed a motor-imagery task in unimodal and bimodal NF conditions. With this protocol we were able to compare for the first time the effects of unimodal EEG-neurofeedback and fMRI-neurofeedback versus bimodal EEG-fMRI-neurofeedback by looking both at EEG and fMRI activations. We also propose a new feedback metaphor for bimodal EEG-fMRI-neurofeedback that integrates both EEG and fMRI signal in a single bi-dimensional feedback (a ball mo...

We propose event-related cortical sources estimation from subject-independent electroencephalography (EEG) recordings for motor imagery brain computer interface (BCI). By using wavelet-based maximum entropy on the mean (wMEM), task-specific EEG channels are selected to predict right hand and right foot sensorimotor tasks, employing common spatial pattern (CSP) and regularized common spatial pattern (RCSP). EEG from five healthy individuals (Dataset IVa, BCI Competition III) were evaluated by a cross-subject paradigm. Prediction performance was evaluated via a two-layer feed-forward neural network, where the classifier was trained and tested by data from two subjects independently. On average, the overall mean prediction accuracies obtained using all 118 channels are (55.98±6.53) and (71.20±5.32) in cases of CSP and RCSP, respectively, which are slightly lower than the accuracies obtained using only the selected channels, i.e., (58.95±6.90) and (71.41±6.65), respectively. The highest mean prediction accuracy achieved for a specific subject pair by using selected EEG channels was on average (90.36±5.59) and outperformed that achieved by using all available channels (86.07 ± 10.71). Spatially projected cortical sources approximated using wMEM may be useful for capturing inter-subject associative sensorimotor brain dynamics and pave the way toward an enhanced subject-independent BCI.