Partiendo del análisis de la normativa estatal y autonómica vigente, este artículo se aproxima a ... more Partiendo del análisis de la normativa estatal y autonómica vigente, este artículo se aproxima a la cuestión relativa a la situación actual del Derecho ambiental en Galicia haciendo hincapié en sus debilidades y fortalezas. En este sentido, el autor llama la atención sobre los problemas que derivan de la ingente proliferación de instrumentos y la dispersión que actualmente dominan el panorama normativo ambiental a nivel autonómico y en especial, sobre el alto riesgo de incumplimiento de la normativa ambiental que provoca esta situación. En atención al problema señalado, culmina esta exposición una propuesta de lege ferenda, útil a su juicio como fórmula para abordarlo.
IntroductionBrain-Computer Interfaces (BCI) can allow control of external devices using motor ima... more IntroductionBrain-Computer Interfaces (BCI) can allow control of external devices using motor imagery (MI) decoded from electroencephalography (EEG). Although BCI have a wide range of applications including neurorehabilitation, the low spatial resolution of EEG, coupled to the variability of cortical activations during MI, make control of BCI based on EEG a challenging task.MethodsAn assessment of BCI control with different feedback timing strategies was performed. Two different feedback timing strategies were compared, comprised by passive hand movement provided by a robotic hand orthosis. One of the timing strategies, the continuous, involved the partial movement of the robot immediately after the recognition of each time segment in which hand MI was performed. The other feedback, the discrete, was comprised by the entire movement of the robot after the processing of the complete MI period. Eighteen healthy participants performed two sessions of BCI training and testing, one with ...
Background: Stroke is the third cause of disability and the first cause of motor impairment of th... more Background: Stroke is the third cause of disability and the first cause of motor impairment of the upper limb, which significantly reduces independence in activities of daily living. Therefore, the development and use of new therapeutic approaches is needed. Brain-computer interfaces (BCI) based on movement intention (MI) have the potential to improve hand function and brain plasticity after stroke. Purpose: Improve hand function after stroke with BCI with MI. Methods: 8 patients with subacute stroke, left or right hemiparesis was divided in two groups. The first received conventional therapy followed by BCI-MI therapy and the second was given BCI-MI therapy and then conventional therapy. The outcome variables were sensory-motor recovery, hand function, spasticity, level of disability, grip strength, change in motor threshold and amplitude of motor evoked potentials (MEP) and changes in desynchronization/synchronization event-related (EDR/EDS). Results: 4 patients were female, the a...
Astrocytoma is one of the most common brain tumors in adults. Surgery is the main treatment, howe... more Astrocytoma is one of the most common brain tumors in adults. Surgery is the main treatment, however, hemiparesis and decreased cortical activations in primary motor areas are common after astrocytoma resection in the frontal lobe [1]. Hence, a possible complementary therapy for hemiparesis in these patients are Brain-Computer Interfaces (BCI), which allow to decode user’s intentions from the central nervous system in order to control an external device, such as a robotic orthosis. It has been hypothesized that BCI interventions can enhance neuroplasticity and elicit motor recovery, with clinical trials mainly aimed at stroke. However, a BCI complementary intervention for hemiparesis treatment in astrocytoma patients has not been reported and could bring further insights on the role of BCI in unassessed neurorehabilitation scenarios. The case of a 32-year-old woman diagnosed at age 20 with astrocytoma after a first tumor resection followed by radiotherapy, is presented. Twelve years after her first resection, she was diagnosed with a grade II astrocytoma in the left frontal lobe and hyperthyroidism. After tumor resection, she presented postoperative right hemiparesis, dysphagia, and speech difficulties. For 19 days, she received language, lower limb physical, and occupational therapy, and regained the ability to walk independently but remained with hand function impairment. The patient was invited to participate in a feasibility study of a BCI for upper limb neurorehabilitation. The BCI intervention was comprised by 30 sessions (5 per week). In each session, the patient sat in a comfortable armchair and was instructed using visual cues to perform the motor intention (MI) of her paralyzed hand. The BCI decoded MI using 11 EEG channels (F3, F4, Fz, C3, C4, Cz, P3, P4, Pz, T3, and T4 positions of the international 10–20 system) and activated a robotic hand orthosis to perform passive finger flexion followed by extension of the patient’s paralyzed hand, with this occurring once per trial if MI was detected. The technical description of the used BCI system is described elsewhere [2]. Figure 1 shows the BCI setup. Each BCI session consisted of 4 runs of 20 trials each (80 trials per session). The Fugl-Meyer Assessment for Upper Extremity (FMA-UE), the Action Research Arm Test (ARAT), and Blood Oxygenation Level-Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) during MI were the outcome measures assessed before, after 15 sessions and at the end of the BCI intervention (30 sessions). fMRI was acquired using a 64-channel head coil with a 3 T Siemens Skyra device. fMRI significant activations during patient’s MI of her paralyzed hand were acquired using a block design. Voxels were compared using a t-test with a confidence level of 95%. Upper limb-focused occupational therapy was also administered to the patient, in parallel with the BCI intervention. Before the BCI intervention, the patient had a moderate impairment according to the FMA-UE (49 points) and ARAT (28 points) baseline scores. Following the 30 sessions of BCI intervention, upper limb motor function improvement was of 11 and 13 points for FMA-UE and ARAT, respectively. These gains in motor function were likely noticeable since they were higher than twice the minimal clinically significant difference for FMA-UE (5.2 points) and ARAT (5.7 points) stroke-related hemiparesis. Furthermore, this degree of recovery was significant since it has been reported that only 12.5% of patients with neurological * Jessica Cantillo-Negrete [email protected]
Brain-Computer Interfaces (BCI) coupled to robotic assistive devices have shown promise for the r... more Brain-Computer Interfaces (BCI) coupled to robotic assistive devices have shown promise for the rehabilitation of stroke patients. However, little has been reported that compares the clinical and physiological effects of a BCI intervention for upper limb stroke rehabilitation with those of conventional therapy. This study assesses the feasibility of an intervention with a BCI based on electroencephalography (EEG) coupled to a robotic hand orthosis for upper limb stroke rehabilitation and compares its outcomes to conventional therapy. Seven subacute and three chronic stroke patients (M = 59.9 ± 12.8) with severe upper limb impairment were recruited in a crossover feasibility study to receive 1 month of BCI therapy and 1 month of conventional therapy in random order. The outcome measures were comprised of: Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), motor evoked potentials elicited by transcranial magnetic stimulation (TMS), hand dynamometry...
Objective. This study assesses upper limb recovery prognosis after stroke with solely physiologic... more Objective. This study assesses upper limb recovery prognosis after stroke with solely physiological information, which can provide an objective estimation of recovery. Approach. Clinical recovery was forecasted using EEG-derived Event-Related Desynchronization/Synchronization and coherence, in addition to Transcranial Magnetic Stimulation elicited motor-evoked potentials and upper limb grip and pinch strength. A Regression Tree Ensemble predicted clinical recovery of a stroke database (n = 10) measured after a two-month intervention with the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE) and the Action Research Arm Test (ARAT). Main results. There were no significant differences between predicted and actual outcomes with FMA-UE (p = 0.29) and ARAT (p = 0.5). Median prediction error for FMA-UE and ARAT were of 0.3 (IQR = 6.2) and 3.4 (IQR = 9.4) points, respectively. Predictions with the most pronounced errors were due to an underestimation of high upper limb recovery. The be...
Medical & Biological Engineering & Computing, 2021
Transcranial magnetic stimulation (TMS) allows the assessment of stroke patients' cortical ex... more Transcranial magnetic stimulation (TMS) allows the assessment of stroke patients' cortical excitability and corticospinal tract integrity, which provide information regarding motor function recovery. However, the extraction of features from motor-evoked potentials (MEP) elicited by TMS, such as amplitude and latency, is performed manually, increasing variability due to observer-dependent subjectivity. Therefore, an automatic methodology could improve MEP analysis, especially in stroke, which increases the difficulty of manual MEP measurements due to brain lesions. A methodology based on time-frequency features of stroke patients' MEPs that allows to automatically select and extract MEP amplitude and latency is proposed. The method was validated using manual measurements, performed by three experts, computed from patients' affected and unaffected hemispheres. Results showed a coincidence of 58.3 to 80% between automatic and manual MEP selection. There were no significant differences between the amplitudes and latencies computed by two of the experts with those obtained with the automatic method, for most comparisons. The median relative error of amplitudes and latencies computed by the automatic method was 5% and 23%, respectively. Therefore, the proposed method has the potential to reduce processing time and improve the computation of MEP features, by eliminating observer-dependent variability due to the subjectivity of manual measurements.
Dendrite ellipsoidal neurons are a novel and different alternative for classification tasks, givi... more Dendrite ellipsoidal neurons are a novel and different alternative for classification tasks, giving competitive results compared with typical classification methods. Based on k-means++ algorithm, the network allows each dendrite to build a hyperellipsoidal in order to assign each incoming pattern \(x_{i}=(x_{1},x_{2},\ldots ,x_{n})^{T}\) to its respective C class. The main disadvantage of this training algorithm is the lack of accuracy in high dimensional datasets. In this research, we solved this problem by training the dendrite ellipsoidal neuron using stochastic gradient descent. Furthermore, electroencephalography data were acquired during two mental conditions (imaginary movements of the left and right hand) in order to test the new training algorithm. The proposed algorithm outperformed the accuracy acquired by a dendrite ellipsoidal neuron based on k-means++ obtaining 76.02% and 62.77%, respectively. Also, the algorithm was compared with multilayer perceptrons and support vector machines which are some of the most common classifiers used to detect motor-related information in brain signals. These achieved an accuracy of 72.38% and 65.81%, respectively.
Motor Imagery based Brain-Computer Interfaces (BCI) have shown potential for the rehabilitation o... more Motor Imagery based Brain-Computer Interfaces (BCI) have shown potential for the rehabilitation of stroke patients. In order to make BCI systems available in the clinical environment new processing stages that increase the number of patients that can use these systems must be developed. This work presents a novel processing stage for BCI systems using the Filter Bank Common Spatial Patterns algorithm for feature extraction and Particle Swarm Optimisation for feature selection. The proposed BCI’s processing stage performance was evaluated with electroencephalography data of six stroke patients, which performed motor imagery of their paralysed hand. Offline tests reached average classification accuracies of 75±8 %. For 4 out of 6 patients, the proposed method showed a statistically significant higher performance (p<0.05) than the Common Spatial Pattern method. Therefore, although a higher sample is needed to confirm the observations, it is possible to significantly improve hand mot...
La imaginacion de movimiento es un proceso cognitivo que consiste en la planeacion de un movimien... more La imaginacion de movimiento es un proceso cognitivo que consiste en la planeacion de un movimiento sin ejecutarlo. En la senal de electroencefalografia, esta planeacion puede decodificarse y, usarse como senal de control para una interface cerebro-computadora. El movimiento y la imaginacion de movimiento de las extremidades del cuerpo generan patrones similares en la senal de EEG en seres humanos, por lo que esta similitud podria usarse para clasificar entre dos tareas de imaginacion de movimiento mano. En este trabajo se registraron 11 canales de electroencefalografia a 4 participantes, dos de ellos sanos y dos con enfermedad vascular cerebral, mientras realizaban ensayos de movimiento real e imaginacion de movimiento de las manos. Se aplico un filtrado espacial tipo laplaciano en los canales centrales C3, Cz y C4, despues se obtuvieron representaciones tiempo-frecuencia de la senal filtrada, en las bandas alfa y beta, mediante transformada Wavelet, tanto para los ensayos de movi...
Motor imagery is a cognitive process which involves a planning of a movement without performing i... more Motor imagery is a cognitive process which involves a planning of a movement without performing its execution. This planning can be decoded from the electroencephalography signal and be used for therapeutic purposes. Self-organized maps are artificial neural networks that have a potential to be used as motor imagery classifiers. In this work 11 channels were recorded from 2 healthy subjects and from 2 stroke patients. The data from the channels was pre-processed with a Laplacian type spatial filter applied to channels C3, Cz and C4, then power features where computed with a wavelet transform in the alpha and beta bands. Self-organized maps were trained and tested for each participant with the power features obtained from 3 different conditions. Correct classification accuracies above the chance level were achieved and even reached 80%. The proposed classifiers are promising for motor imagery decoding in the EEG signal, the performance of the proposed classifiers can be increased if ...
Transcranial magnetic stimulation can be used as a tool for assessment of corticospinal tract int... more Transcranial magnetic stimulation can be used as a tool for assessment of corticospinal tract integrity by extracting features from elicited motor evoked potentials (MEPs). However, the selection of MEPs from recordings that present them, and the computation of amplitude and latency are usually performed manually. A methodology based on computing summed power calculated by means of the Discrete Fourier Transform in 10-ms windows was used for automatization of this process and compared to manual measurements. Results showed that MEP selection coincided in 69% of cases, and amplitude and latency had average differences of 7% between methods. Therefore, automatization of MEP computations is feasible using frequency-based features.
Brain-computer interfaces (BCI) decode user's intentions to control external devices. However, pe... more Brain-computer interfaces (BCI) decode user's intentions to control external devices. However, performance variations across individuals have limited their use to laboratory environments. Handedness could contribute to these variations, especially when motor imagery (MI) tasks are used for BCI control. To further understand how handedness affects BCI control, performance differences between two monozygotic twins were analysed during offline movement and MI tasks, and while twins controlled a BCI using right-hand MI. Quantitative electroencephalography (qEEG), brain structures' volumes, and neuropsychological tests were assessed to evaluate physiological, anatomical and psychological relationships with BCI performance. Results showed that both twins had good motor imagery and attention abilities, similar volumes on most subcortical brain structures, more pronounced event-related desynchronization elicited by the twin performing non-dominant MI, and that this twin also obtained significant higher performances with the BCI. Linear regression analysis implied a strong association between twins' BCI performance, and more pronounced cortical activations in the contralateral hemisphere relative to hand MI. Therefore, it is possible that BCI performance was related with the ability of each twin to elicit cortical activations during hand MI, and less associated with subcortical brain structures' volumes and neuropsychological tests.
Stroke is a leading cause of motor disability worldwide. Upper limb rehabilitation is particularl... more Stroke is a leading cause of motor disability worldwide. Upper limb rehabilitation is particularly challenging since approximately 35% of patients recover significant hand function after 6 months of the stroke’s onset. Therefore, new therapies, especially those based on brain-computer interfaces (BCI) and robotic assistive devices, are currently under research. Electroencephalography (EEG) acquired brain rhythms in alpha and beta bands, during motor tasks, such as motor imagery/intention (MI), could provide insight of motor-related neural plasticity occurring during a BCI intervention. Hence, a longitudinal analysis of subacute stroke patients’ brain rhythms during a BCI coupled to robotic device intervention was performed in this study. Data of 9 stroke patients were acquired across 12 sessions of the BCI intervention. Alpha and beta event-related desynchronization/synchronization (ERD/ERS) trends across sessions and their association with time since stroke onset and clinical upper...
Brain-Computer Interfaces (BCI) allow the control of external devices by decoding the users' inte... more Brain-Computer Interfaces (BCI) allow the control of external devices by decoding the users' intentions from their central nervous system. Feedback, one of the main elements of a closedloop BCI, is used to enhance the user's performance. The present work aimed to compare the effect of two different feedback sources; congruent anatomical visual hand representation and passive hand movement on BCI performance and cortical activations. Electroencephalography of 12 healthy right-handed subjects was recorded to set a BCI activated by right-hand motor imagery. Afterward, the subjects were asked to control the system by imagining the movement. The system provided either visual feedback, shown on a computer screen or kinesthetic feedback, provided by a robotic hand orthosis. Differences in performance and cortical activations were assessed, using classification accuracy and event-related desynchronization/synchronization in m and b bands, respectively. Performance was significantly better with kinesthetic feedback as it allowed for higher correct classification of motor imagery. Cortical activations in the ipsilateral central channel in m were different between the two feedback modalities. Our results imply that healthy subjects can achieve a greater degree of control using a motor imagery-based BCI with kinesthetic feedback than with anatomically congruent visual feedback. Furthermore, cortical activation differences show that kinesthetic feedback seems to elicit higher recruitment of sensorimotor cortex brain cells, which probably reflects enhanced local information modulation related to fine motor processing. Therefore, kinesthetic feedback provided by a robotic orthosis could be a more suitable feedback strategy for BCI systems designed for neuromodulation and neurorehabilitation.
Motor imagery-based brain-computer interfaces (BCI) have shown potential for the rehabilitation o... more Motor imagery-based brain-computer interfaces (BCI) have shown potential for the rehabilitation of stroke patients; however, low performance has restricted their application in clinical environments. Therefore, this work presents the implementation of a BCI system, coupled to a robotic hand orthosis and driven by hand motor imagery of healthy subjects and the paralysed hand of stroke patients. A novel processing stage was designed using a bank of temporal filters, the common spatial pattern algorithm for feature extraction and particle swarm optimisation for feature selection. Offline tests were performed for testing the proposed processing stage, and results were compared with those computed with common spatial patterns. Afterwards, online tests with healthy subjects were performed in which the orthosis was activated by the system. Stroke patients’ average performance was 74.1 ± 11%. For 4 out of 6 patients, the proposed method showed a statistically significant higher performance ...
Motor imagery-based brain-computer interfaces decode users' intentions from the electroencephalog... more Motor imagery-based brain-computer interfaces decode users' intentions from the electroencephalogram; however, poor spatial resolution makes automatic recognition of these intentions a challenging task. New classification approaches with low computational costs and high classification performances need to be developed in order to increase the number of users benefitted by these systems. On the other hand, spiking neuron models, which are mathematical abstractions of real neurons, have shown good performances in several classification tasks, making these models suitable for motor imagery classification. In this work, two different encoding strategies for spiking neuron models, applied to the classification of motor imagery time-frequency features of stroke patients and healthy subjects, were evaluated. Classification performances and computational costs of spiking neuron models were compared against those of linear discriminant analysis, support vector machines and artificial neural networks. Results showed that a time-varying encoding strategy is more suitable for motor imagery classification, and its implementation computational cost is low. Therefore, a spiking neuron model with a time-varying encoding strategy could increase the number of potential users of braincomputer interfaces.
Brain computer interface systems (BCI) translate the intentions of patients affected with locked-... more Brain computer interface systems (BCI) translate the intentions of patients affected with locked-in syndrome through the EEG signal characteristics, which are converted into commands used to control external devices. One of the strategies used, is to decode the motor imagery of the subject, which can modify the neuronal activity in the sensory-motor areas in a similar way to which it is observed in real movement. The present study shows the activation patterns that are registered in motor and motor imagery tasks of right and left hand movement in a sample of young healthy subjects of Mexican nationality. By means of frequency analysis it was possible to determine the difference conditions of motor imagery and movement. Using U Mann-Whitney tests, differences with statistical significance (p < 0.05) where obtained, in the EEG channels C3, Cz, C4, T3 and P3 in the mu and beta rhythms, for subjects with similar characteristics (age, gender, and education). With these results, it would be possible to define a classifier or decoder by gender that improves the performance rate and diminishes the training time, with the goal of designing a functional BCI system that can be transferred from the laboratory to the clinical application in patients with motor disabilities.
Spiking neural networks (SNN) have been successfully applied in pattern classification problems. ... more Spiking neural networks (SNN) have been successfully applied in pattern classification problems. However, their performance for solving complex problems such as electroencephalography (EEG) classification has not been widely assessed. It is necessary to consider new approaches to select relevant information and for training SNN in order to improve their accuracy when applied to complex data classification. In this paper, we present a novel channel selection and classification method based on SNN trained with Particle Swarm Optimization (PSO) for the classification of EEG signals associated to motor imagery. The proposed method was able to correctly identify the most relevant channels for different motor imagery tasks. The SNN trained with PSO achieved good classification performances for a well-studied public database using a minimal number of EEG channels, showing advantages against other approaches, regarding both performance and system requirements.
Partiendo del análisis de la normativa estatal y autonómica vigente, este artículo se aproxima a ... more Partiendo del análisis de la normativa estatal y autonómica vigente, este artículo se aproxima a la cuestión relativa a la situación actual del Derecho ambiental en Galicia haciendo hincapié en sus debilidades y fortalezas. En este sentido, el autor llama la atención sobre los problemas que derivan de la ingente proliferación de instrumentos y la dispersión que actualmente dominan el panorama normativo ambiental a nivel autonómico y en especial, sobre el alto riesgo de incumplimiento de la normativa ambiental que provoca esta situación. En atención al problema señalado, culmina esta exposición una propuesta de lege ferenda, útil a su juicio como fórmula para abordarlo.
IntroductionBrain-Computer Interfaces (BCI) can allow control of external devices using motor ima... more IntroductionBrain-Computer Interfaces (BCI) can allow control of external devices using motor imagery (MI) decoded from electroencephalography (EEG). Although BCI have a wide range of applications including neurorehabilitation, the low spatial resolution of EEG, coupled to the variability of cortical activations during MI, make control of BCI based on EEG a challenging task.MethodsAn assessment of BCI control with different feedback timing strategies was performed. Two different feedback timing strategies were compared, comprised by passive hand movement provided by a robotic hand orthosis. One of the timing strategies, the continuous, involved the partial movement of the robot immediately after the recognition of each time segment in which hand MI was performed. The other feedback, the discrete, was comprised by the entire movement of the robot after the processing of the complete MI period. Eighteen healthy participants performed two sessions of BCI training and testing, one with ...
Background: Stroke is the third cause of disability and the first cause of motor impairment of th... more Background: Stroke is the third cause of disability and the first cause of motor impairment of the upper limb, which significantly reduces independence in activities of daily living. Therefore, the development and use of new therapeutic approaches is needed. Brain-computer interfaces (BCI) based on movement intention (MI) have the potential to improve hand function and brain plasticity after stroke. Purpose: Improve hand function after stroke with BCI with MI. Methods: 8 patients with subacute stroke, left or right hemiparesis was divided in two groups. The first received conventional therapy followed by BCI-MI therapy and the second was given BCI-MI therapy and then conventional therapy. The outcome variables were sensory-motor recovery, hand function, spasticity, level of disability, grip strength, change in motor threshold and amplitude of motor evoked potentials (MEP) and changes in desynchronization/synchronization event-related (EDR/EDS). Results: 4 patients were female, the a...
Astrocytoma is one of the most common brain tumors in adults. Surgery is the main treatment, howe... more Astrocytoma is one of the most common brain tumors in adults. Surgery is the main treatment, however, hemiparesis and decreased cortical activations in primary motor areas are common after astrocytoma resection in the frontal lobe [1]. Hence, a possible complementary therapy for hemiparesis in these patients are Brain-Computer Interfaces (BCI), which allow to decode user’s intentions from the central nervous system in order to control an external device, such as a robotic orthosis. It has been hypothesized that BCI interventions can enhance neuroplasticity and elicit motor recovery, with clinical trials mainly aimed at stroke. However, a BCI complementary intervention for hemiparesis treatment in astrocytoma patients has not been reported and could bring further insights on the role of BCI in unassessed neurorehabilitation scenarios. The case of a 32-year-old woman diagnosed at age 20 with astrocytoma after a first tumor resection followed by radiotherapy, is presented. Twelve years after her first resection, she was diagnosed with a grade II astrocytoma in the left frontal lobe and hyperthyroidism. After tumor resection, she presented postoperative right hemiparesis, dysphagia, and speech difficulties. For 19 days, she received language, lower limb physical, and occupational therapy, and regained the ability to walk independently but remained with hand function impairment. The patient was invited to participate in a feasibility study of a BCI for upper limb neurorehabilitation. The BCI intervention was comprised by 30 sessions (5 per week). In each session, the patient sat in a comfortable armchair and was instructed using visual cues to perform the motor intention (MI) of her paralyzed hand. The BCI decoded MI using 11 EEG channels (F3, F4, Fz, C3, C4, Cz, P3, P4, Pz, T3, and T4 positions of the international 10–20 system) and activated a robotic hand orthosis to perform passive finger flexion followed by extension of the patient’s paralyzed hand, with this occurring once per trial if MI was detected. The technical description of the used BCI system is described elsewhere [2]. Figure 1 shows the BCI setup. Each BCI session consisted of 4 runs of 20 trials each (80 trials per session). The Fugl-Meyer Assessment for Upper Extremity (FMA-UE), the Action Research Arm Test (ARAT), and Blood Oxygenation Level-Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) during MI were the outcome measures assessed before, after 15 sessions and at the end of the BCI intervention (30 sessions). fMRI was acquired using a 64-channel head coil with a 3 T Siemens Skyra device. fMRI significant activations during patient’s MI of her paralyzed hand were acquired using a block design. Voxels were compared using a t-test with a confidence level of 95%. Upper limb-focused occupational therapy was also administered to the patient, in parallel with the BCI intervention. Before the BCI intervention, the patient had a moderate impairment according to the FMA-UE (49 points) and ARAT (28 points) baseline scores. Following the 30 sessions of BCI intervention, upper limb motor function improvement was of 11 and 13 points for FMA-UE and ARAT, respectively. These gains in motor function were likely noticeable since they were higher than twice the minimal clinically significant difference for FMA-UE (5.2 points) and ARAT (5.7 points) stroke-related hemiparesis. Furthermore, this degree of recovery was significant since it has been reported that only 12.5% of patients with neurological * Jessica Cantillo-Negrete [email protected]
Brain-Computer Interfaces (BCI) coupled to robotic assistive devices have shown promise for the r... more Brain-Computer Interfaces (BCI) coupled to robotic assistive devices have shown promise for the rehabilitation of stroke patients. However, little has been reported that compares the clinical and physiological effects of a BCI intervention for upper limb stroke rehabilitation with those of conventional therapy. This study assesses the feasibility of an intervention with a BCI based on electroencephalography (EEG) coupled to a robotic hand orthosis for upper limb stroke rehabilitation and compares its outcomes to conventional therapy. Seven subacute and three chronic stroke patients (M = 59.9 ± 12.8) with severe upper limb impairment were recruited in a crossover feasibility study to receive 1 month of BCI therapy and 1 month of conventional therapy in random order. The outcome measures were comprised of: Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), motor evoked potentials elicited by transcranial magnetic stimulation (TMS), hand dynamometry...
Objective. This study assesses upper limb recovery prognosis after stroke with solely physiologic... more Objective. This study assesses upper limb recovery prognosis after stroke with solely physiological information, which can provide an objective estimation of recovery. Approach. Clinical recovery was forecasted using EEG-derived Event-Related Desynchronization/Synchronization and coherence, in addition to Transcranial Magnetic Stimulation elicited motor-evoked potentials and upper limb grip and pinch strength. A Regression Tree Ensemble predicted clinical recovery of a stroke database (n = 10) measured after a two-month intervention with the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE) and the Action Research Arm Test (ARAT). Main results. There were no significant differences between predicted and actual outcomes with FMA-UE (p = 0.29) and ARAT (p = 0.5). Median prediction error for FMA-UE and ARAT were of 0.3 (IQR = 6.2) and 3.4 (IQR = 9.4) points, respectively. Predictions with the most pronounced errors were due to an underestimation of high upper limb recovery. The be...
Medical & Biological Engineering & Computing, 2021
Transcranial magnetic stimulation (TMS) allows the assessment of stroke patients' cortical ex... more Transcranial magnetic stimulation (TMS) allows the assessment of stroke patients' cortical excitability and corticospinal tract integrity, which provide information regarding motor function recovery. However, the extraction of features from motor-evoked potentials (MEP) elicited by TMS, such as amplitude and latency, is performed manually, increasing variability due to observer-dependent subjectivity. Therefore, an automatic methodology could improve MEP analysis, especially in stroke, which increases the difficulty of manual MEP measurements due to brain lesions. A methodology based on time-frequency features of stroke patients' MEPs that allows to automatically select and extract MEP amplitude and latency is proposed. The method was validated using manual measurements, performed by three experts, computed from patients' affected and unaffected hemispheres. Results showed a coincidence of 58.3 to 80% between automatic and manual MEP selection. There were no significant differences between the amplitudes and latencies computed by two of the experts with those obtained with the automatic method, for most comparisons. The median relative error of amplitudes and latencies computed by the automatic method was 5% and 23%, respectively. Therefore, the proposed method has the potential to reduce processing time and improve the computation of MEP features, by eliminating observer-dependent variability due to the subjectivity of manual measurements.
Dendrite ellipsoidal neurons are a novel and different alternative for classification tasks, givi... more Dendrite ellipsoidal neurons are a novel and different alternative for classification tasks, giving competitive results compared with typical classification methods. Based on k-means++ algorithm, the network allows each dendrite to build a hyperellipsoidal in order to assign each incoming pattern \(x_{i}=(x_{1},x_{2},\ldots ,x_{n})^{T}\) to its respective C class. The main disadvantage of this training algorithm is the lack of accuracy in high dimensional datasets. In this research, we solved this problem by training the dendrite ellipsoidal neuron using stochastic gradient descent. Furthermore, electroencephalography data were acquired during two mental conditions (imaginary movements of the left and right hand) in order to test the new training algorithm. The proposed algorithm outperformed the accuracy acquired by a dendrite ellipsoidal neuron based on k-means++ obtaining 76.02% and 62.77%, respectively. Also, the algorithm was compared with multilayer perceptrons and support vector machines which are some of the most common classifiers used to detect motor-related information in brain signals. These achieved an accuracy of 72.38% and 65.81%, respectively.
Motor Imagery based Brain-Computer Interfaces (BCI) have shown potential for the rehabilitation o... more Motor Imagery based Brain-Computer Interfaces (BCI) have shown potential for the rehabilitation of stroke patients. In order to make BCI systems available in the clinical environment new processing stages that increase the number of patients that can use these systems must be developed. This work presents a novel processing stage for BCI systems using the Filter Bank Common Spatial Patterns algorithm for feature extraction and Particle Swarm Optimisation for feature selection. The proposed BCI’s processing stage performance was evaluated with electroencephalography data of six stroke patients, which performed motor imagery of their paralysed hand. Offline tests reached average classification accuracies of 75±8 %. For 4 out of 6 patients, the proposed method showed a statistically significant higher performance (p<0.05) than the Common Spatial Pattern method. Therefore, although a higher sample is needed to confirm the observations, it is possible to significantly improve hand mot...
La imaginacion de movimiento es un proceso cognitivo que consiste en la planeacion de un movimien... more La imaginacion de movimiento es un proceso cognitivo que consiste en la planeacion de un movimiento sin ejecutarlo. En la senal de electroencefalografia, esta planeacion puede decodificarse y, usarse como senal de control para una interface cerebro-computadora. El movimiento y la imaginacion de movimiento de las extremidades del cuerpo generan patrones similares en la senal de EEG en seres humanos, por lo que esta similitud podria usarse para clasificar entre dos tareas de imaginacion de movimiento mano. En este trabajo se registraron 11 canales de electroencefalografia a 4 participantes, dos de ellos sanos y dos con enfermedad vascular cerebral, mientras realizaban ensayos de movimiento real e imaginacion de movimiento de las manos. Se aplico un filtrado espacial tipo laplaciano en los canales centrales C3, Cz y C4, despues se obtuvieron representaciones tiempo-frecuencia de la senal filtrada, en las bandas alfa y beta, mediante transformada Wavelet, tanto para los ensayos de movi...
Motor imagery is a cognitive process which involves a planning of a movement without performing i... more Motor imagery is a cognitive process which involves a planning of a movement without performing its execution. This planning can be decoded from the electroencephalography signal and be used for therapeutic purposes. Self-organized maps are artificial neural networks that have a potential to be used as motor imagery classifiers. In this work 11 channels were recorded from 2 healthy subjects and from 2 stroke patients. The data from the channels was pre-processed with a Laplacian type spatial filter applied to channels C3, Cz and C4, then power features where computed with a wavelet transform in the alpha and beta bands. Self-organized maps were trained and tested for each participant with the power features obtained from 3 different conditions. Correct classification accuracies above the chance level were achieved and even reached 80%. The proposed classifiers are promising for motor imagery decoding in the EEG signal, the performance of the proposed classifiers can be increased if ...
Transcranial magnetic stimulation can be used as a tool for assessment of corticospinal tract int... more Transcranial magnetic stimulation can be used as a tool for assessment of corticospinal tract integrity by extracting features from elicited motor evoked potentials (MEPs). However, the selection of MEPs from recordings that present them, and the computation of amplitude and latency are usually performed manually. A methodology based on computing summed power calculated by means of the Discrete Fourier Transform in 10-ms windows was used for automatization of this process and compared to manual measurements. Results showed that MEP selection coincided in 69% of cases, and amplitude and latency had average differences of 7% between methods. Therefore, automatization of MEP computations is feasible using frequency-based features.
Brain-computer interfaces (BCI) decode user's intentions to control external devices. However, pe... more Brain-computer interfaces (BCI) decode user's intentions to control external devices. However, performance variations across individuals have limited their use to laboratory environments. Handedness could contribute to these variations, especially when motor imagery (MI) tasks are used for BCI control. To further understand how handedness affects BCI control, performance differences between two monozygotic twins were analysed during offline movement and MI tasks, and while twins controlled a BCI using right-hand MI. Quantitative electroencephalography (qEEG), brain structures' volumes, and neuropsychological tests were assessed to evaluate physiological, anatomical and psychological relationships with BCI performance. Results showed that both twins had good motor imagery and attention abilities, similar volumes on most subcortical brain structures, more pronounced event-related desynchronization elicited by the twin performing non-dominant MI, and that this twin also obtained significant higher performances with the BCI. Linear regression analysis implied a strong association between twins' BCI performance, and more pronounced cortical activations in the contralateral hemisphere relative to hand MI. Therefore, it is possible that BCI performance was related with the ability of each twin to elicit cortical activations during hand MI, and less associated with subcortical brain structures' volumes and neuropsychological tests.
Stroke is a leading cause of motor disability worldwide. Upper limb rehabilitation is particularl... more Stroke is a leading cause of motor disability worldwide. Upper limb rehabilitation is particularly challenging since approximately 35% of patients recover significant hand function after 6 months of the stroke’s onset. Therefore, new therapies, especially those based on brain-computer interfaces (BCI) and robotic assistive devices, are currently under research. Electroencephalography (EEG) acquired brain rhythms in alpha and beta bands, during motor tasks, such as motor imagery/intention (MI), could provide insight of motor-related neural plasticity occurring during a BCI intervention. Hence, a longitudinal analysis of subacute stroke patients’ brain rhythms during a BCI coupled to robotic device intervention was performed in this study. Data of 9 stroke patients were acquired across 12 sessions of the BCI intervention. Alpha and beta event-related desynchronization/synchronization (ERD/ERS) trends across sessions and their association with time since stroke onset and clinical upper...
Brain-Computer Interfaces (BCI) allow the control of external devices by decoding the users' inte... more Brain-Computer Interfaces (BCI) allow the control of external devices by decoding the users' intentions from their central nervous system. Feedback, one of the main elements of a closedloop BCI, is used to enhance the user's performance. The present work aimed to compare the effect of two different feedback sources; congruent anatomical visual hand representation and passive hand movement on BCI performance and cortical activations. Electroencephalography of 12 healthy right-handed subjects was recorded to set a BCI activated by right-hand motor imagery. Afterward, the subjects were asked to control the system by imagining the movement. The system provided either visual feedback, shown on a computer screen or kinesthetic feedback, provided by a robotic hand orthosis. Differences in performance and cortical activations were assessed, using classification accuracy and event-related desynchronization/synchronization in m and b bands, respectively. Performance was significantly better with kinesthetic feedback as it allowed for higher correct classification of motor imagery. Cortical activations in the ipsilateral central channel in m were different between the two feedback modalities. Our results imply that healthy subjects can achieve a greater degree of control using a motor imagery-based BCI with kinesthetic feedback than with anatomically congruent visual feedback. Furthermore, cortical activation differences show that kinesthetic feedback seems to elicit higher recruitment of sensorimotor cortex brain cells, which probably reflects enhanced local information modulation related to fine motor processing. Therefore, kinesthetic feedback provided by a robotic orthosis could be a more suitable feedback strategy for BCI systems designed for neuromodulation and neurorehabilitation.
Motor imagery-based brain-computer interfaces (BCI) have shown potential for the rehabilitation o... more Motor imagery-based brain-computer interfaces (BCI) have shown potential for the rehabilitation of stroke patients; however, low performance has restricted their application in clinical environments. Therefore, this work presents the implementation of a BCI system, coupled to a robotic hand orthosis and driven by hand motor imagery of healthy subjects and the paralysed hand of stroke patients. A novel processing stage was designed using a bank of temporal filters, the common spatial pattern algorithm for feature extraction and particle swarm optimisation for feature selection. Offline tests were performed for testing the proposed processing stage, and results were compared with those computed with common spatial patterns. Afterwards, online tests with healthy subjects were performed in which the orthosis was activated by the system. Stroke patients’ average performance was 74.1 ± 11%. For 4 out of 6 patients, the proposed method showed a statistically significant higher performance ...
Motor imagery-based brain-computer interfaces decode users' intentions from the electroencephalog... more Motor imagery-based brain-computer interfaces decode users' intentions from the electroencephalogram; however, poor spatial resolution makes automatic recognition of these intentions a challenging task. New classification approaches with low computational costs and high classification performances need to be developed in order to increase the number of users benefitted by these systems. On the other hand, spiking neuron models, which are mathematical abstractions of real neurons, have shown good performances in several classification tasks, making these models suitable for motor imagery classification. In this work, two different encoding strategies for spiking neuron models, applied to the classification of motor imagery time-frequency features of stroke patients and healthy subjects, were evaluated. Classification performances and computational costs of spiking neuron models were compared against those of linear discriminant analysis, support vector machines and artificial neural networks. Results showed that a time-varying encoding strategy is more suitable for motor imagery classification, and its implementation computational cost is low. Therefore, a spiking neuron model with a time-varying encoding strategy could increase the number of potential users of braincomputer interfaces.
Brain computer interface systems (BCI) translate the intentions of patients affected with locked-... more Brain computer interface systems (BCI) translate the intentions of patients affected with locked-in syndrome through the EEG signal characteristics, which are converted into commands used to control external devices. One of the strategies used, is to decode the motor imagery of the subject, which can modify the neuronal activity in the sensory-motor areas in a similar way to which it is observed in real movement. The present study shows the activation patterns that are registered in motor and motor imagery tasks of right and left hand movement in a sample of young healthy subjects of Mexican nationality. By means of frequency analysis it was possible to determine the difference conditions of motor imagery and movement. Using U Mann-Whitney tests, differences with statistical significance (p < 0.05) where obtained, in the EEG channels C3, Cz, C4, T3 and P3 in the mu and beta rhythms, for subjects with similar characteristics (age, gender, and education). With these results, it would be possible to define a classifier or decoder by gender that improves the performance rate and diminishes the training time, with the goal of designing a functional BCI system that can be transferred from the laboratory to the clinical application in patients with motor disabilities.
Spiking neural networks (SNN) have been successfully applied in pattern classification problems. ... more Spiking neural networks (SNN) have been successfully applied in pattern classification problems. However, their performance for solving complex problems such as electroencephalography (EEG) classification has not been widely assessed. It is necessary to consider new approaches to select relevant information and for training SNN in order to improve their accuracy when applied to complex data classification. In this paper, we present a novel channel selection and classification method based on SNN trained with Particle Swarm Optimization (PSO) for the classification of EEG signals associated to motor imagery. The proposed method was able to correctly identify the most relevant channels for different motor imagery tasks. The SNN trained with PSO achieved good classification performances for a well-studied public database using a minimal number of EEG channels, showing advantages against other approaches, regarding both performance and system requirements.
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Papers by Ruben Escobar