Independent ambulation is a challenging problem for quadriplegics, since most have little functional muscle for an external human-machine interface (HMI), whereas invasive controls are expensive and dangerous. A viable option is the use of surface electroencephalography (s-EEG) based Brain Computer Interface (BCI) that can be achieved using affordable mobile EEG devices like the Emotiv EPOC. This, along with modern machine learning techniques, can allow the implementation of a fully functional BCI to establish an external control system for an assistive device, such as a prosthetic arm or an electric wheelchair.
Multiple methods that were designed for controlling a wheelchair were explored. The main differences observed were in the number of electrodes, their configuration, number of controls, and the methods of feature extraction and classification. The work focused on implementing and comparing two real-time BCI implementations of a 2-state BCI based on linear-support vector machines (linear SVM) classifier. Its performance was analyzed on two s-EEG databases: Physionet EEG Motor Movement/Imagery dataset and EEG data collected in our lab using the Emotiv EPOC BCI device. The Physionet data contains 64 channels, while the Emotiv data contain 14 channels. The accuracy and speed were compared for motor intent and motor action for both the electrode configurations.
A comparison of the differences in covariance matrix of the transformed data between the two input classes revealed that the common spatial patterns feature extraction approach led up to 50\% greater differences in features than that of the common spatial frequency subspace decomposition.
|Commitee:||Mondin, Marina, Daneshgaran, Fereydoun|
|School:||California State University, Los Angeles|
|Department:||Electrical and Computer Engineering|
|School Location:||United States -- California|
|Source:||MAI 81/2(E), Masters Abstracts International|
|Subjects:||Electrical engineering, Biomedical engineering, Engineering|
|Keywords:||Brain computer interface (BCI), Common spatial patterns, Electroencephalography (EEG), Linear support vector machines (Linear-SVM), Machine learning, Wavelet transform|
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