A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

Abstract

Multiple types of brain-control systems have been applied in the field of rehabilitation. As an alternative scheme for balancing user fatigue and the classification accuracy of brain–computer interface (BCI) systems, facial-expression-based brain control technologies have been proposed in the form of novel BCI systems. Unfortunately, existing machine learning algorithms fail to identify the most relevant features of electroencephalogram signals, which further limits the performance of the classifiers. To address this problem, an improved classification method is proposed for facial-expression-based BCI (FE-BCI) systems, using a convolutional neural network (CNN) combined with a genetic algorithm (GA). The CNN was applied to extract features and classify them. The GA was used for hyperparameter selection to extract the most relevant parameters for classification. To validate the superiority of the proposed algorithm used in this study, various experimental performance results were systematically evaluated, and a trained CNN-GA model was constructed to control an intelligent car in real time. The average accuracy across all subjects was 89.21 ± 3.79%, and the highest accuracy was 97.71 ± 2.07%. The superior performance of the proposed algorithm was demonstrated through offline and online experiments. The experimental results demonstrate that our improved FE-BCI system outperforms the traditional methods.

A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance

“…Steady-state visual evoked potentials (SSVEPs) based on BCI systems have the advantages of short training time, high signal-to-noise ratio, and short response time, and are widely used in clinical detection technology ( Li et al, 2022 ). When an external visual stimulus of constant frequencies is applied, the neural network consistent with the stimulation frequency or harmonic components will generate resonance, causing the brain’s potential activity to change significantly at the stimulation frequency or harmonic components, resulting in SSVEP signals.…”

A quantization algorithm of visual fatigue based on underdamped second order stochastic resonance for steady state visual evoked potentials

“…In recent years, as visual fatigue is closely related to brain state, relevant studies have also pointed out that measuring brain state can also evaluate visual fatigue to a certain extent (Li et al, 2017(Li et al, , 2023Li W.-K. et al, 2022). Here, nonlinear algorithms are widely used (Li R. et al, 2022), which also provides ideas for the algorithm in this paper.…”

Study on the diversity of mental states and neuroplasticity of the brain during human-machine interaction

“…Moreover, with the emergence and development of non-invasive brain function monitoring technologies, such as electroencephalogram (EEG) ( Li et al, 2022 ), magnetoencephalography (MEG) ( Baillet, 2017 ), functional magnetic resonance imaging (fMRI) ( Power et al, 2017 ), the interrelationship between human perception, cognition and performance, systems, and technology can be studied from many perspectives. In terms of mental state, for example, Li et al (2019) designed a mental arithmetic task to induce mental fatigue in the subjects.…”