Generalized Time Domain Prediction Model for Motor imagery-based Wheelchair Movement Control
DOI:
https://doi.org/10.58496/ADSA/2024/008Keywords:
Brain-Computer Interface, Wheelchair, Control, Transfer Learning, Pattern Recognition, Generalized Model, Time-DomainAbstract
Principally, Brain-computer interface (BCI-MI)-based wheelchair control is an appropriate method for completely paralyzed people with healthy brains. In a BCI-based wheelchair control system, the procedure of pattern recognition in terms of preprocessing, feature extraction, and classification plays a significant role in avoiding recognition errors, which can lead to the initiation of the wrong command that will put the user in unsafe condition. Therefore, the objective of this research is to develop a time domain generic pattern recognition model (GPRM) for two classes of EEG-MI signals for use in a wheelchair control system. The advantage of such a GPRM is that it has a model that can also be applied for unknown subjects and is not suitable for only one subject. This GPRM has been developed, evaluated, and validated by utilizing two datasets, namely, the BCI Competition IV and Emotive EPOC datasets. Initially, fifteen time windows were investigated with seven machine learning methods to determine the optimal time window as well as the best classification method with strong generalizability. Evidently, the experimental results of this study revealed that the duration of the EEG-MI signal in the range of 4 to 6 s (4-6 s) had a high impact on the classification accuracy when the signal features were extracted using five statistical methods. Additionally, the results showed that there was a one-second latency after each command cue when utilizing the eight-second EEG-MI signal recorded by the Graz protocol applied in this study. This one second latency is inevitable because it is practically impossible for the subjects to imagine their MI hand movement instantly. Therefore, at least one second is required for subjects to prepare to initiate their motor imagery hand movement. Practically, the five statistical methods are efficient and viable for decoding the EEG-MI signal in the time domain. Evidently, the GPRM model based on the LR classifier showed generalizability by attaining an impressive percentage classification accuracy of 90%, which was validated on the Emotive EPOC dataset. Overall, the findings suggest that the GPRM developed in this study is highly adaptable and is recommended for use in real-time applications of EEG-MI-based wheelchair control systems.
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Copyright (c) 2024 Z.T. Al-Qaysi , M. S Suzani , Nazre bin Abdul Rashid , Reem D. Ismail , M.A. Ahmed , Rasha A. Aljanabi, Mohd Arfian Ismail
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