Electroencephalography (EEG) records the electrical signals produced by the brain using an array of electrodes placed on the scalp. Computers use an algorithm called common spatial pattern (CSP) to translate these signals into commands for the control of various devices.
Haiping Lu at the A*A*STAR Institute for Infocomm Research and co-workers[1] have now developed an improved version of CSP for classifying EEG signals. The new algorithm will facilitate the development of advanced brain–computer interfaces that may one day enable paralyzed patients to control devices such as computers and robotic arms.
CSP distinguishes and interprets commands by estimating the variations between EEG signals, and its accuracy strongly depends on how many signals are provided. As a result, CSP may make an incorrect interpretation when the number of EEG signals is small.
The new CSP algorithm developed by Lu and his colleagues uses two parameters to regularize the estimation of EEG signal variations. One parameter lowers the variations of the estimates, while the other reduces the tendency of the CSP algorithm to bias the estimates towards values from only a small number of samples.
Together, these parameters significantly improve the accuracy of CSP for classifying EEG signals. The researchers optimized the new algorithm even further by aggregating a number of different regularizations.
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