Abstract
Recently, Electroencephalography (EEG) motor imagery (MI) signals have received increasing attention because it became possible to use these signals to encode a person’s intention to perform an action. Researchers have used MI signals to help people with partial or total paralysis, control devices such as exoskeletons, wheelchairs, prostheses, and even independent driving. Therefore, classifying the motor imagery tasks of these signals is important for a Brain-Computer Interface (BCI) system. Classifying the MI tasks from EEG signals is difficult to offer a good decoder due to the dynamic nature of the signal, its low signal-to-noise ratio, complexity, and dependence on the sensor positions. In this paper, we investigate five multilayer methods for classifying MI tasks: proposed methods based on Artificial Neural Network, Convolutional Neural Network 1 (CNN1), CNN2, CNN1 with CNN2 merged, and the modified CNN1 with CNN2 merged. These proposed methods use different spatial and temporal characteristics extracted from raw EEG data. We demonstrate that our proposed CNN1-based method outperforms state-of-the-art machine/deep learning techniques for EEG classification by an accuracy value of 68.77% and use spatial and frequency characteristics on the BCI Competition IV-2a dataset, which includes nine subjects performing four MI tasks (left/right hand, feet, and tongue). The experimental results demonstrate the feasibility of this proposed method for the classification of MI-EEG signals and can be applied successfully to BCI systems where the amount of data is large due to daily recording.
Highlights
This article is an open access articleIn recent years, the use of brain signals from EEG electroencephalography has been widely explored for various applications with a major focus on the field of biomedical engineering
Brain-Computer Interface (BCI) were initially developed for people with disabilities, and in particular for those suffering from what is known as “locked-in syndrome” [2] which is a neurological pathology
Our aims are oriented towards the high precision requirements of the classification of motor imagery (MI) tasks of the EEG signal
Summary
This article is an open access articleIn recent years, the use of brain signals from EEG electroencephalography has been widely explored for various applications with a major focus on the field of biomedical engineering. A Brain-Computer Interface (BCI) system, referred to as brain-machine interaction, bridges the gap between humans and computers by translating thoughts into commands, which can be used to communicate with external devices like exoskeletons, distributed under the terms and conditions of the Creative Commons. Thought-based control of machines is a hot topic that is increasingly integrated into various applications. BCIs were initially developed for people with disabilities, and in particular for those suffering from what is known as “locked-in syndrome” [2] which is a neurological pathology. The patient with this disease is usually quadriplegic and cannot move or speak. His consciousness and his cognitive and intellectual faculties are intact
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