Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI

Sundaresan, Alamelu; Penchina, Brian; Cheong, Sean; Grace, Victoria; Valero‐Cabré, Antoni; Martel, Adrien

doi:10.1186/s40708-021-00133-5

Cited by 37 publications

(17 citation statements)

References 93 publications

(86 reference statements)

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“…Although MI-EEGNET performed better than previous architectures, the high number of trainable parameters made it challenging to interpret. Besides these CNN architectures investigated for EEG signal classification, several studies can be found in the literature that used recurrent neural network (RNN) and its variants [21][22][23], such as LSTM and gated recurrent units (GRU), for the classification of mental tasks based on EEG signals [24,25]. However, RNNs are less prevalent in this area due to their exploding/vanishing gradient or lack of memory problems [26].…”

Section: Related Workmentioning

confidence: 99%

EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification

2022

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In recent years, neural networks and especially deep architectures have received substantial attention for EEG signal analysis in the field of brain-computer interfaces (BCIs). In this ongoing research area, the end-to-end models are more favoured than traditional approaches requiring signal transformation pre-classification. They can eliminate the need for prior information from experts and the extraction of handcrafted features. However, although several deep learning algorithms have been already proposed in the literature, achieving high accuracies for classifying motor movements or mental tasks, they often face a lack of interpretability and therefore are not quite favoured by the neuroscience community. The reasons behind this issue can be the high number of parameters and the sensitivity of deep neural networks to capture tiny yet unrelated discriminative features. We propose an end-to-end deep learning architecture called EEG-ITNet and a more comprehensible method to visualise the network learned patterns. Using inception modules and causal convolutions with dilation, our model can extract rich spectral, spatial, and temporal information from multi-channel EEG signals with less complexity (in terms of the number of trainable parameters) than other existing end-to-end architectures, such as EEG-Inception and EEG-TCNet. By an exhaustive evaluation on dataset 2a from BCI competition Ⅳ and OpenBMI motor imagery dataset, EEG-ITNet shows up to 5.9% improvement in the classification accuracy in different scenarios with statistical significance compared to its competitors. We also comprehensively explain and support the validity of network illustration from a neuroscientific perspective. We have also made our code freely accessible at https://github.com/AbbasSalami/EEG-ITNet.

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Section: Related Workmentioning

confidence: 99%

EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification

2022

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“…Ref. [31] 5 Classification: Adaptive neurofuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS).…”

Section: Asd Classification/detectionmentioning

confidence: 99%

“…In reference [30], they used an ML and a DL process for diagnosing ASD from time-frequency spectrogram images of EEG. The authors in [31] reported that it is possible to evaluate mental stress using DL and EEG records. There are also studies such as [32], where they used the free artifact signal of two electrodes to detect ASD.…”

Section: Introductionmentioning

confidence: 99%

Attention Measurement of an Autism Spectrum Disorder User Using EEG Signals: A Case Study

Esqueda-Elizondo

Juárez‐Ramírez

López-Bonilla

et al. 2022

MCA

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Autism Spectrum Disorder (ASD) is a neurodevelopmental life condition characterized by problems with social interaction, low verbal and non-verbal communication skills, and repetitive and restricted behavior. People with ASD usually have variable attention levels because they have hypersensitivity and large amounts of environmental information are a problem for them. Attention is a process that occurs at the cognitive level and allows us to orient ourselves towards relevant stimuli, ignoring those that are not, and act accordingly. This paper presents a methodology based on electroencephalographic (EEG) signals for attention measurement in a 13-year-old boy diagnosed with ASD. The EEG signals are acquired with an Epoc+ Brain–Computer Interface (BCI) via the Emotiv Pro platform while developing several learning activities and using Matlab 2019a for signal processing. For this article, we propose to use electrodes F3, F4, P7, and P8. Then, we calculate the band power spectrum density to detect the Theta Relative Power (TRP), Alpha Relative Power (ARP), Beta Relative Power (BRP), Theta–Beta Ratio (TBR), Theta–Alpha Ratio (TAR), and Theta/(Alpha+Beta), which are features related to attention detection and neurofeedback. We train and evaluate several machine learning (ML) models with these features. In this study, the multi-layer perceptron neural network model (MLP-NN) has the best performance, with an AUC of 0.9299, Cohen’s Kappa coefficient of 0.8597, Matthews correlation coefficient of 0.8602, and Hamming loss of 0.0701. These findings make it possible to develop better learning scenarios according to the person’s needs with ASD. Moreover, it makes it possible to obtain quantifiable information on their progress to reinforce the perception of the teacher or therapist.

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“…When deep learning becomes the consensus and normal state of educational practice and the essence of learning returns, the name of “deep learning” may return to “learning” instead of emphasizing “depth”. It is worth mentioning that deep learning is relative to false learning and mechanical learning, and the latter two are not what school teaching should be ( Sakalle et al, 2021 ; Sundaresan et al, 2021 ). Each computing layer of the network is composed of multiple feature maps, and each feature map exists in the form of a two-dimensional plane.…”

Section: Deep Learning Researchmentioning

confidence: 99%

Analysis and research on the influence of music on students’ mental health under the background of deep learning

Wang

Zhao²,

Yin³

2022

Front. Psychol.

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This paper makes a detailed analysis and discussion on the impact of music appreciation on college students’ mental health and the influence of music appreciation on students’ mental health, mental energy and mental structure. There has long been the idea of music promoting people’s mental health, as well as related research in the field of music psychology. For this specific group of primary and secondary school students, it should be said that it is relatively rare to consider using music education to promote their mental health. This paper summarizes the advantages of deep learning over shallow learning, explains the necessity of introducing deep learning, and describes the data representation of deep learning and several typical deep learning models. This study adopts the method of multi-evidence to conduct in-depth research and analysis. On the basis of in-depth study and research, this paper analyzes and studies the impact of music on students’ mental health, so as to lay a foundation for future research on students’ mental health. In terms of influencing factors and strategies to promote students’ in-depth learning, we should apply the research results to specific teaching situations with the help of advanced digital technology, and strive to combine theory with practice. The research shows that college students’ mental health is an important part of quality education in Colleges and universities, and music education plays an important role in the implementation of quality education.

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Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI

Cited by 37 publications

References 93 publications

EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification

EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification

Attention Measurement of an Autism Spectrum Disorder User Using EEG Signals: A Case Study

Analysis and research on the influence of music on students’ mental health under the background of deep learning

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