Abstract
BackgroundRecently, successful applications of the discrete wavelet transform have been reported in brain interface (BI) systems with one or two EEG channels. For a multi-channel BI system, however, the high dimensionality of the generated wavelet features space poses a challenging problem.MethodsIn this paper, a feature selection method that effectively reduces the dimensionality of the feature space of a multi-channel, self-paced BI system is proposed. The proposed method uses a two-stage feature selection scheme to select the most suitable movement-related potential features from the feature space. The first stage employs mutual information to filter out the least discriminant features, resulting in a reduced feature space. Then a genetic algorithm is applied to the reduced feature space to further reduce its dimensionality and select the best set of features.ResultsAn offline analysis of the EEG signals (18 bipolar EEG channels) of four able-bodied subjects showed that the proposed method acquires low false positive rates at a reasonably high true positive rate. The results also show that features selected from different channels varied considerably from one subject to another.ConclusionThe proposed hybrid method effectively reduces the high dimensionality of the feature space. The variability in features among subjects indicates that a user-customized BI system needs to be developed for individual users.
Highlights
Successful applications of the discrete wavelet transform have been reported in brain interface (BI) systems with one or two EEG channels
Our findings showed that a 5th degree polynomial kernel function performed better than other kernel functions studied (linear, polynomial with a degree other than 5 (3, 4, 6 and 7) and RBF kernel)
The columns 1 to 5 of Table 2 show the subject identification number, the average true positive (TP) rate on the test sets, the average false positive (FP) rate on the TP test sets, the average ratio and the average number of FP features selected by the hybrid feature selection process
Summary
Successful applications of the discrete wavelet transform have been reported in brain interface (BI) systems with one or two EEG channels. A successful brain interface (BI) system enables individuals with severe motor disabilities to control objects in their environment (such as a light switch, neural prosthesis or computer) by using only their brain signals. Such a system measures specific features of a person's brain signal that relate to his or her intent to affect control, translates them into control signals that are used to control a device [1,2]. In a self-paced BI system, users can affect the output of the BI system whenever they want, by intentionally changing their brain state.
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