BCI Brain Computer Interface

Control & Communication Systems Based on Acquisition & Processing of Brain Signals

A brain–computer interface (BCI), sometimes called a neural-control interface (NCI), mind-machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCI differs from neuromodulation in that it allows for bidirectional information flow. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature. The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.[4] Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s. Refrence: wikipedia


Arain Computer Interface BCI


Signal Acquisition:

Brain signals are recorded on the scalp of the users using electrodes, which are mounted on an EEG cap. This happens non-invasive, no harm is done to the user.

Signal Preprocessing:

The measured signals are quite weak, even eye-blinks greatly influence them. Therefore, complex algorithms are applied to enhance the signal quality to reveal the brain patterns

Decoding/Encoding:

Preprocessed signals are analyzed with modern machine learn methods to identify brain patterns of the designated imaginations

Control and Feedback:

Every action should cause a adequate reaction. If you grasp a glass, you do feel the glass beyond your fingers, get a measure of its weight and feel the temperature of it. This "feedback" helps us performing our tasks of daily life without even fully recognizing them - like adjusting the grasp force we put on the glass when we "feel" that it is heavier than expected. For a person with no sensation in the hand, this sensations cannot be felt anymore. Therefore substitutes have to be implemented - which are called feedback.




A BCI system works as a closed loop system. Every action the user performs results in any form of feedback to user. An imagined hand movement for instance, could resolve in a command which triggers the movement of a (neuro)prosthesis. The user, allthough he may not be able to feel the movement due to spinal cord injury (SCI), sees the (neuro)prosthesis move his arm. This visual feedback closes the loop. BCI use is a skill that users must learn. Although first basis control could be established within a couple of training sessions, usually up to one in four control attempts might be negative. Various studies suggest that intensive BCI training enable users to overcome this limitation. Training BCI skills is often fatiguing and monotonous. Usually users have to repeat different motor imageries, which will later on used to train modern machine learning algorithms and eventually generate control signals out of the individual brain patterns. During this monotonous repetitions, the user often does not get any feedback in how well he performs these actions. Recent studies showed that it is possible to change this by individual adaptation of the system already during the training. Within the Moregrasp project, BCI will be one of the control modalities for the users. BCI training will happen right away from the start and is an essential part of the individual training for each user. We are aiming to exploit recent adaptive approaches to make training far more interesting than monotonous repetitions of different movements - and we strongly believe that this effort will have positive effects on our users too! Refrence: Link



My Current Activities & Researches

- EEGLAB preprocessing - Importing raw data
- EEGLAB preprocessing - Events and channel locations
- EEGLAB preprocessing - Rereferencing and resampling
- EEGLAB preprocessing - Filtering
- EEGLAB preprocessing - Visualizing data
- EEGLAB preprocessing - Removing bad channels
- EEGLAB preprocessing - Removing bad data segments




EGGLAB

What is EEGLAB? EEGLAB is an interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. EEGLAB runs under Linux, Unix, Windows, and Mac OS X. refrence: link



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Morteza Karimian Kelishadrokhi

Excellent knowledge in Analytical, Problem-solving, & Troubleshooting ability

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Morteza Karimian.AI