Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system

Chai, Rifai; Tran, Yvonne; Craig, Ashley; Ling, Sai Ho; Nguyen, Hung T.

doi:10.1109/embc.2014.6943846

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…These are biocompatible implants for neural recordings, devices for stimulating neural circuits, and wireless recording systems. BCIs can connect the brain to computer cursors (Carmena et al, 2003; Lebedev et al, 2005), text generators (Pan et al, 2013; Akram et al, 2014), arm prostheses (Carmena et al, 2003; Velliste et al, 2008; Collinger et al, 2013), exoskeletons for assisted walking (Gancet et al, 2011; Contreras-Vidal and Grossman, 2013; Kwak et al, 2015), virtual-reality objects (Badia et al, 2013), powered wheelchairs (Galán et al, 2008; Chai et al, 2014), drones (LaFleur et al, 2013), and automobiles (Göhring et al, 2013). Recently, futuristic BCIs have emerged that merge several individual brains into a brain-net (Pais-Vieira et al, 2013; Rao et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Decoding Movement From Electrocorticographic Activity: A Review

Volkova

Lebedev

Kaplan

et al. 2019

Front. Neuroinform.

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Electrocorticography (ECoG) holds promise to provide efficient neuroprosthetic solutions for people suffering from neurological disabilities. This recording technique combines adequate temporal and spatial resolution with the lower risks of medical complications compared to the other invasive methods. ECoG is routinely used in clinical practice for preoperative cortical mapping in epileptic patients. During the last two decades, research utilizing ECoG has considerably grown, including the paradigms where behaviorally relevant information is extracted from ECoG activity with decoding algorithms of different complexity. Several research groups have advanced toward the development of assistive devices driven by brain-computer interfaces (BCIs) that decode motor commands from multichannel ECoG recordings. Here we review the evolution of this field and its recent tendencies, and discuss the potential areas for future development.

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Section: Introductionmentioning

confidence: 99%

Decoding Movement From Electrocorticographic Activity: A Review

Volkova

Lebedev

Kaplan

et al. 2019

Front. Neuroinform.

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“…These signals can be acquired either invasively [1] or noninvasively [2]. Both approaches have witnessed significant advancements in areas such as motor decoding [3][4][5][6], speech restoration [7][8][9], and emotion recognition [10][11][12]. Machine learning, particularly deep learning, has garnered increasing attention across diverse domains such as computer vision [13,14] and natural language processing [15,16].…”

Section: Introductionmentioning

confidence: 99%

Data augmentation for invasive brain–computer interfaces based on stereo-electroencephalography (SEEG)

Wu,

Zhang,

et al. 2024

J. Neural Eng.

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Abstract—Objective: : Deep learning is increasingly used for Brain-computer interfaces (BCIs). However, the available brain data is sparse, especially for invasive BCIs, which can dramatically deteriorate deep learning performance. Data augmentation methods (DA), such as generative models, can help to address this issue. However, existing studies on brain signals relied on convolutional neural networks (CNNs) and ignored the temporal dependence. This paper tried to enhance the generative model by capturing the temporal relationship from a time-series perspective.Methods: A conditional generative network (cTGAN) based on the transformer model is proposed, and tested on the Stereo- electroencephalography (SEEG) data which was recorded from eight epileptic patients performing five different movements. Three other commonly-used DA methods were also implemented: noise injection (NI), variational autoencoder (VAE) and conditional Wasserstein GAN with gradient penalty (cWGANGP). Artificial SEEG data was generated, and various metrics were used to compare the data quality, including visual inspection, Cosine similarity (CS), Jensen-Shannon distance (JSD) and the effect on the performance of a deep learning-based classifier.Result: Both the proposed cTGAN and the cWGANGP methods were able to generate realistic data, while NI and VAE output inferior samples when visualised as raw sequences and in a lower dimensional space. The cTGAN generated the best samples in terms of cosine similarity and Jensen-Shannon distance and outperformed cWGANGP significantly in enhancing the performance of a deep learning-based classifier (each of them yielding a significant improvement of 6% and 3.4%, respectively).Conclusion: This paper demonstrated that a generative model that preserves temporal dependence is superior in data generation and boosting deep learning performance for SEEG signals.Significance: This is the first time that DA methods are applied to invasive BCIs based on SEEG. In addition, this study demonstrated the advantages of the model that preserves the temporal dependence from a time-series perspective.

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“…Artificial neural networks (ANNs) with a hidden layer and support vector machines (SVM) are well-known shallow models. Of these, ANNs have been widely used in EEG fatigue detection systems (Chai et al, 2014 ). In the literature (Vuckovic et al, 2002 ), to predict fatigue status from EEG signal, time series of inter- and intra-hemispheric cross-spectrogram densities of EEG signal are fed as input to an ANN, which then classifies driver status as either fatigue or alertness.…”

Section: Introductionmentioning

confidence: 99%

“…This mental fatigue reduces a driver's ability to concentrate and make decisions, making it impossible to drive effectively. According to data released by China's National Bureau of Statistics, more than 60,000 people will die in traffic accidents nationwide in 2020 alone (Bureau, 2021). Traffic accidents cause great harm and loss to individuals, the country, and society.…”

Section: Introductionmentioning

confidence: 99%

EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks

Gao

Xue²,

Wan³

et al. 2023

Front. Neurosci.

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Driver fatigue detection is one of the essential tools to reduce accidents and improve traffic safety. Its main challenge lies in the problem of how to identify the driver's fatigue state accurately. Existing detection methods include yawning and blinking based on facial expressions and physiological signals. Still, lighting and the environment affect the detection results based on facial expressions. In contrast, the electroencephalographic (EEG) signal is a physiological signal that directly responds to the human mental state, thus reducing the impact on the detection results. This paper proposes a log-Mel spectrogram and Convolution Recurrent Neural Network (CRNN) model based on EEG to implement driver fatigue detection. This structure allows the advantages of the different networks to be exploited to overcome the disadvantages of using them individually. The process is as follows: first, the original EEG signal is subjected to a one-dimensional convolution method to achieve a Short Time Fourier Transform (STFT) and passed through a Mel filter bank to obtain a logarithmic Mel spectrogram, and then the resulting logarithmic Mel spectrogram is fed into a fatigue detection model to complete the fatigue detection task for the EEG signals. The fatigue detection model consists of a 6-layer convolutional neural network (CNN), bi-directional recurrent neural networks (Bi-RNNs), and a classifier. In the modeling phase, spectrogram features are transported to the 6-layer CNN to automatically learn high-level features, thereby extracting temporal features in the bi-directional RNN to obtain spectrogram-temporal information. Finally, the alert or fatigue state is obtained by a classifier consisting of a fully connected layer, a ReLU activation function, and a softmax function. Experiments were conducted on publicly available datasets in this study. The results show that the method can accurately distinguish between alert and fatigue states with high stability. In addition, the performance of four existing methods was compared with the results of the proposed method, all of which showed that the proposed method could achieve the best results so far.

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Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system

Cited by 6 publications

References 13 publications

Decoding Movement From Electrocorticographic Activity: A Review

Decoding Movement From Electrocorticographic Activity: A Review

Data augmentation for invasive brain–computer interfaces based on stereo-electroencephalography (SEEG)

EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks

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