Abstract
The motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has been receiving attention from neural engineering researchers and is being applied to various rehabilitation applications. However, the performance degradation caused by motor imagery EEG with very low single-to-noise ratio faces several application issues with the use of a BCI system. In this paper, we propose a novel motor imagery classification scheme based on the continuous wavelet transform and the convolutional neural network. Continuous wavelet transform with three mother wavelets is used to capture a highly informative EEG image by combining time-frequency and electrode location. A convolutional neural network is then designed to both classify motor imagery tasks and reduce computation complexity. The proposed method was validated using two public BCI datasets, BCI competition IV dataset 2b and BCI competition II dataset III. The proposed methods were found to achieve improved classification performance compared with the existing methods, thus showcasing the feasibility of motor imagery BCI.
Highlights
Brain-Computer Interface (BCI) translates brain signals into an interpretable output without the direct use of peripheral nerves and muscles
The phenomenon in which the power of both frequency bands of the EEG signals is restored after the motor imagery (MI) tasks is called event-related synchronization (ERS)
The event-related desynchronization (ERD)/ERS patterns are calculated as the rate of the change of power with respect to the reference signals, which are given in Equations (8)–(10) [36]: EEGavg( j) =
Summary
Brain-Computer Interface (BCI) translates brain signals into an interpretable output without the direct use of peripheral nerves and muscles. The primary purpose of BCI is to create a communication system through brain signals without physical movement for people with severe motor disabilities [1]. Event-related potential (e.g., P300), steady-state visual evoked potential (SSVEP), and motor imagery (MI) have attracted attention in the BCI research community. The MI BCI model has been widely used since it can be applied to control external devices [2]. The MI BCI approach is increasingly being applied in various fields, including games [3] and assistive technology [4]. Even for the remaining BCI users, those with motor disabilities, MI BCI might not offer the best mental option for BCI control [1,6].
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