SPARK - Take Brain Selfies of your Neuronal Activity

New portable EEG devices are in the market every year. Now the Personal Neuro Devices company are launching the SPARK ($99) that connects directly with your smartphone.

The ability to take and send brain images to your smartphone is only the first step. The Spark environment allows developers and innovators to create their own apps and programs. Harness your Spark data and expand realm of what is possible for pervasive neurotechnology, brain monitoring and treatment.

Features

• Sampling Rate: 256 Hz;
• Bluetooth v.4;
• Battery Life: up to 8 hours;
• Neurofeedback apps to train cognitive faculties.

Have you ever taken a selfie of your brain? This is one of the many things you can do with Spark. Get an image of your brain sent directly to your phone. Keep track of your mental well-being and the state of your brain using up to date neurometrics. It's a selfie that gets below the surface.

It's probably the most easyer EEG equipment to use in the market. No doubt that this is the way for portable BCI but I am curious to know about the EEG results, from AF3 and AF4 and a few more electrodes, processed and classified in a smartphone.

For more information about BCI/EEG press here.

The future of BCI - From the state of the art to Vision

The BNCI Horizon 2020 Consortium have just released the final version of the roadmap for the field of brain-computer interfaces (BCIs).

• Who should read this roadmap?
• Aim and Executive summary
• What is a BCI and Vision for 2025
• Use case scenarios
• Unlocking the locked-in and NeuroTutor
• BCI-controlled neuroprosthesis
• Hybrid BCI-driven FES for stroke rehabilitation
• Research tool for cognitive neuroscience
• Horizon 2020 and the place for BCIs
• BCI market, stakeholders and Ethics
• General recommendations
• Appendix
• Research, Industry and End Users
• Use cases, focus groups and BCI Society
• Consortium

Vision for 2025

"In 2025, a wide array of applications will use brain signals as an important source of information. We will see routine applications in professional context, personal health monitoring, and medical treatment. We envision a future where humans and information technology are seamlessly and intuitively connected by integrating various biosignals, particularly brain activity. People will be supported in choosing the best time for making difficult and important decisions. People working in safety-relevant fields will be capable of anticipating fatigue, and authorities may find good (evidence-based) reasons to incorporate such applications in regulations. (...)"

The following graph shows the potential that the BCI has for the next two decades:

It's also possible to download the summarized roadmapping activities. For more informations visit bnci-horizon-2020.eu

3rd International Winter BCI Conference - 49 Publications

All the links to the 49 publications from the 3rd International BCI Conference, presented in this blog some weeks ago, are available here or at IEEE Xplore Digital Library.

• Tutorial on multimodal neuroimaging for brain-computer interfacing
• Bayesian common spatial patterns
• Brain-computer interface for stroke rehabilitation with clinical studies
• Mobile real-time EEG imaging Bayesian inference with sparse, temporally smooth source priors
• Neuro-driving: Automatic perception technique for upcoming emergency situations
• Spatial projections of neural arrays: A short guide to classic and new signal analysis techniques
• Divergence estimation for machine learning and signal processing
• Progress toward a high-performance neural prosthetic
• Decoding cognitive brain states
• Eeg/sonication-based brain-brain interfacing
• Japanese SRPBS for BMI research: Decoded neurofeedback as a causal tool in systems neuroscience
• A Bayesian approach for spatio-spectral filter optimization in BCI
• A novel P300-based BCI system for words typing
• Non-homogeneous spatial filter optimization for EEG-based brain-computer interfaces
• A novel tactile stimulation system for BCI feedback
• A study on information transfer rate by brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS)
• BMFLC with neural network and DE for better event classification
• Classifying ECoG signals prior to voluntary movement onset
• Common spatial patterns based on generalized norms
• Decoding three-dimensional arm movements for brain-machine interface
• Design of a robotic wheelchair with a motor imagery based brain-computer interface
• Detection of multi-class emergency situations during simulated driving from ERP
• Dry electrode design and performance evaluation for EEG based BCI systems
• EEG asymmetry and anxiety
• Eeg-based emotion recogntion during emotionally evocative films
• Eeg-based person authentication using face stimuli
• Effects of rest frames on cybersickness and oscillatory brain activity
• Efficient edge detection method for anatomic feature extraction of neuro-sensory tissue image based on optical coherence tomography
• Emotion classification in movie clips based on 3D fuzzy GIST and EEG signal analysis
• Estimation on the development of cerebral edema from computed tomography preliminary studies for pediatric traumatic brain injury patients
• Ethical and social issues behind brain-computer interface
• Evaluation of feature extraction methods for motor imagery-based bcis in terms of robustness to slight changes of electrode locations
• “Eyes-closed” SSVEP-based BCI for binary communication of individuals with impaired oculomotor function
• Implementation of a mental spelling system based on steady-state visual evoked potential (SSVEP)
• Learning Markov random field image prior for pixelation removal of fiber microscopy using sparse coding based on Bayesian framework
• Modified pattern-reversal visual checkerboard stimuli with dual alternating frequencies for multi-class ssvep-based brain-computer interfaces
• Motor trajectory decoding based on fMRI-based BCI — A simulation study
• Multimodal imaging technique for rapid response brain-computer interface feedback
• Optimal channel selection based on statistical analysis in high dimensional NIRS data
• P300 speller using a new stimulus presentation paradigm
• Power consumption of wireless EEG device for BCI application: Portable EEG system for BCI
• The beginning of neurohaptics: Controlling cognitive interaction via brain haptic interface
• The effectiveness of epidural ECoG on brain computer interface in primate
• Three-dimensional upper limb movement decoding from EEG signals

iPhone Mobile App that Reads your Brain Activity

MELON company from USA created a headband, very simple to use, that reads your brain activity. This device uses the electroencephalography to measure your focus level and process it to show the results in your iPhone.

Gain insight into how your mind works by tracking your focus during any activity you choose. Understand yourself. Learn differently. Imagine if you could see and understand the invisible activity of your brain. Could you transform how you work? Improve the way you meditate? Find a new way to be creative? Melon is a headband and mobile app duo that tracks and helps you improve your focus in relation to your activity, your environment, your emotions, and any other behavior you want to track.

For the developer community there are a Software Development Kit for iOS and Android apps, where it is possible to access to raw data, list of algorithms for different mental states and connect to and control other Bluetooth devices

This project is undoubtedly one step further to the future of BCI.