Abstract
Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this problem, this paper presents a multi-channel convolutional neural network architecture with adaptively optimized parameters. Our solution outperforms alternative methods in terms of classification accuracy of mental tasks (imagination of hand movements and speech sounds generation) while providing high generalization capability (∼5%). Classification efficiency was obtained by using a frequency-domain multi-channel neural network feeding scheme by EEG signal frequency sub-bands analysis and architecture supporting feature mapping with two subsequent convolutional layers terminated with a fully connected layer. For dataset V from BCI Competition III, the method achieved an average classification accuracy level of nearly 70%, outperforming alternative methods. The solution presented applies a frequency domain for input data processed by a multi-channel architecture that isolates frequency sub-bands in time windows, which enables multi-class signal classification that is highly generalizable and more accurate (∼1.2%) than the existing solutions. Such an approach, combined with an appropriate learning strategy and parameters optimization, adapted to signal characteristics, outperforms reference single- or multi-channel networks, such as AlexNet, VGG-16 and Cecotti’s multi-channel NN. With the classification accuracy improvement of 1.2%, our solution is a clear advance as compared to the top three state-of-the-art methods, which achieved the result of no more than 0.3%.
Highlights
A brain–computer interface (BCI) enables communication between the brain and external systems by means of messages and commands originating from users’ thoughts rather than from the physical activity of body parts or the operations of dedicated electronic controllers [1]
Research has tended to focus on single-channel neural networks and their architecture, with experiments concerning mostly the number of layers, kernel characteristics affected by input data stream or the learning strategy [5,6,7,8,10,11,12]
Cecotti experimented with a multi-channel convolutional neural network architecture, dedicating the network channels to different time domain windows for signal analysis [9]
Summary
A brain–computer interface (BCI) enables communication between the brain and external systems by means of messages and commands originating from users’ thoughts rather than from the physical activity of body parts or the operations of dedicated electronic controllers [1]. The major problems faced during semantic EEG signal analysis (mental tasks classification) are connected with the classification accuracy. Recent solutions proposed in the literature rely on EEG signal factorization using time-domain data stream windowing and classification with convolutional neural networks [5,6,7,8,9]. Research has tended to focus on single-channel neural networks and their architecture, with experiments concerning mostly the number of layers, kernel characteristics affected by input data stream or the learning strategy [5,6,7,8,10,11,12]. Cecotti experimented with a multi-channel convolutional neural network architecture, dedicating the network channels to different time domain windows for signal analysis [9]
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