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Type :thesis
Subject :TK Electrical engineering. Electronics Nuclear engineering
Main Author :Al-Qaysi, Ziadoon Tareq AbdulWahhab
Title :Generic pattern recognition models based on EEG-MI brain computer interfaces for wheelchair steering control
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2020
Corporate Name :Universiti Pendidikan Sultan Idris
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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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