Newsletters - MATLAB Digest
EEG Data Processing and Classification with g.BSanalyze Under MATLAB®
(Part 1 of 6)
by Günter Edlinger and Christoph Guger,
g.tec
Guger Technologies OEG
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Advances in the acquisition and analysis of biosignals such as electroencephalograms (EEGs) and electrocorticograms (ECoGs) are profoundly improving brain wave research, creating opportunities to bypass severed nerve pathways to control prostheses and allow movement of paralyzed body parts. This article describes how products from g.tec, which were built on MATLAB® and Simulink®, can be used to perform this multimodal acquisition and analysis.
In 1929, Hans Berger performed the first noninvasive measurements of bioelectrical activity in the brain. During the last seven decades, electroencephalography, or EEG, has been established as a tool for monitoring brain dynamics and brain function.
g.tec, a MathWorks Connections Partner based in Graz, Austria, develops hardware and software for biosignal processing. They have used distinct EEG patterns acquired during feedback experiments to develop a brain-computer-interface (BCI). A sequence of processing steps can be performed with g.BSanalyze software from g.tec to implement a BCI. This sequence involves displaying and training a person on specific visual stimuli, recording an EEG, and analyzing the EEG using artifact control and feature extraction by filtering common spatial patterns. Data classification is then performed via a linear discriminant analysis. After a training period, the subject is able to control a horizontal bar on the computer screen with an accuracy of nearly 100% simply by imagining the movement of a limb.
EEG Measurement and Applications
An EEG is measured noninvasively using small electrodes that are attached to the surface of the scalp. The number of electrodes can vary from one to 256. The electrodes are placed at certain predefined positions according to the international 10/20 system or variants of that system. The weak electrical activity detected by the electrodes ranges from 5 to 100 µV, and the frequency range of interest is between 1 - 40 Hz.
The EEG recording can provide clues about the physical and mental state of the subject. For example, an EEG that shows alpha waves with high amplitudes over the occipital area, a specific part of the brain, indicates that the subject is relaxed and has his eyes closed. If the subject opens his eyes, the Alpha waves will disappear or desynchronize. In addition, sleep researchers use whole night sleep recordings to investigate and classify different sleep stages. EEGs in epileptic patients can also help in localizing epileptic activity in the brain.
The EEG is typically applied in a stimulus-response scenario, measuring the brain's response to cognitive exercises or auditory, tactile, or visual stimuli. Depending on the kind of stimuli and further data processing steps, either phase-locked signals ("evoked potentials") or non-phase locked signals ("event-related synchronization/desynchronization, ERS/ERD") can be investigated.
Phase-locked signals are an effective means of performing diagnostics. These signals are measured after visual, auditory, or tactile stimuli are presented to a patient. By measuring these signals, you can confirm if specific brain pathways are working properly. The phase-locked signal in the brain should react consistently each time the subject is exposed to a particular stimulus. For example, if you present a tone to the subject and you find out that the shape of the resulting evoked potential is different to the normal case, this indicates a problem with the subject's auditory system.
Non-phase locked changes in the EEG can be observed in hand movement experiments and even in experiments when the subjects only imagines a hand movement.
