UPSI Digital Repository (UDRep)
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Abstract : Universiti Pendidikan Sultan Idris |
The purpose of this study was to develop generic pattern recognition models (GPRMs)
based on two-class EEG–MI brain-computer interfaces for wheelchair steering control.
Initially, a preprocessing procedure was performed to remove unwanted signals and to
identify the optimal duration of MI feature components. Then, feature extraction based
on five statistical features, namely min, max, mean, median, and standard deviation were
utilized for extracting the MI feature components in three signal domains, namely time,
frequency, and time-frequency domains. Seven classification algorithms, namely LDA,
SVM, KNN, ANN, NB, DT, and LR were selected and tested to find the best algorithms
that could be used for the development of hybrid classifiers. Two datasets were used,
namely the BCI Competition dataset (which belonged to Graz University) and the
Emotive EPOC dataset (which was collected in this study), with the former being
utilized in the development, evaluation, and validation of the GPRM models and the
latter being used for validation only. The research findings showed that GPRM models
based on the LR classifier were highly accurate in the time and time-frequency domains
in the range of 4 and 6 seconds and 4 and 7 seconds, respectively. In addition, GPRM
models based on the MLP-LR classifier were highly accurate in the frequency domain
in the range of 4 and 6 seconds. Furthermore, the validation of such models using the
Emotive EPOC dataset showed that the LR-based GPRM model attained high
classification accuracies of 90.2% and 85.7% in the time domain and time-frequency
domain, respectively. The MLP-LR-based GPRM models achieved a classification
accuracy of 84.2% in the frequency domain. In conclusion, the main findings showed
that GPRMs were highly adaptable when deployed in the real-time application of the
EEG-MI-based wheelchair steering control system. The implication of this study is that
generic pattern recognition models based on EEG-MI Brain-Computer interfaces can be
utilized to improve the effectiveness of wheelchair steering control.
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