Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals

Rahman, Atta Ur; Tubaishat, Abdallah; Al-Obeidat, Feras; Halim, Zahid; Tahir, Madiha; Qayum, Fawad

doi:10.1007/s00500-022-06847-w

Cited by 15 publications

(5 citation statements)

References 34 publications

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“…The proposed model only considers the EEG signals, which cannot fully capture the complexity of human emotions. Therefore, in the future work, some other advanced deep learning networks, such as graph neural networks (GNN) (Li et al, 2022a), and BiLSTM (Rahman et al, 2022), new emotion recognition architectures will be introduced to enhance the classification performance of EEG emotion recognition tasks. Correspondence concerning this article should be addressed to Weisi Yang, Department of General and Liberal Arts Education, Sichuan Vocational College of Finance and Economics, Chengdu, 610000, China, E-mail: yangchuancai2023@163.com Dan Fu was born in Sichuan, China, in 1985,Female,ethnic Han,associate professor,she…”

Section: Discussionmentioning

confidence: 99%

Channel Semantic Enhancement-Based Emotional Recognition Method Using SCLE-2D-CNN

Fu,

Yang,

Pan

2024

International Journal on Semantic Web and Information Systems

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The existing EEG emotion classification methods have some problems, such as insufficient emotion representation and lack of targeted channel enhancement module due to feature redundancy. To this end, a novel EEG emotion recognition method (SCLE-2D-CNN) combining scaled convolutional layer (SCLs), enhanced channel module and two-dimensional convolutional neural network (2D-CNN) is proposed. Firstly, the time-frequency features of multi-channel EEG emotional signals were extracted by stacking scl layer by layer. Secondly, channel enhancement module is used to reassign different importance to all EEG physical channels. Finally, 2D-CNN was used to obtain deep local spatiotemporal features and complete emotion classification. The experimental results show that the accuracy of SEED data set and F1 are 98.09% and 97.00%, respectively, and the binary classification accuracy of DEAP data set is 98.06% and 96.83%, respectively, which are superior to other comparison methods. The proposed method has a certain application prospect in the recognition of human mental state.

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Section: Discussionmentioning

confidence: 99%

Channel Semantic Enhancement-Based Emotional Recognition Method Using SCLE-2D-CNN

Fu,

Yang,

Pan

2024

International Journal on Semantic Web and Information Systems

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“…On extended independent component analysis (ICA), a multi-class joint spatial model-based moving window technique and bi-LSTM model to accurately determine mental stress levels from EEG signals have been addressed by [31]. The study published in [32] created a DL framework employing BERT models that offer an efficient approach for predicting the transition of MCI to AD using clinical note processing. Time series data have been the subject of several studies for categorization and forecasting.…”

Section: Related Workmentioning

confidence: 99%

Prediction of mild cognitive impairment using EEG signal and BiLSTM network

Alahmadi,

Rahman,

Alhababi

et al. 2024

Mach. Learn.: Sci. Technol.

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Mild Cognitive Impairment (MCI) is a cognitive disease that primarily affects elderly persons. Patients with MCI have impairments in one or more cognitive areas, such as memory, attention, language, and problem-solving. The risk of Alzheimer's disease (AD) development is 10 times higher among individuals who meet the MCI diagnosis than in those who do not have such a diagnosis. Identifying the primary neurophysiological variations between those who are suffering from cognitive impairment and those who are ageing normally may provide helpful techniques to assess the effectiveness of therapies. Event-related Potentials (ERPs) are utilized to investigate the processing of sensory, cognitive, and motor information in the brain. ERPs enable excellent temporal resolution of underlying brain activity. ERP data is complex due to the temporal variation occurs in time domain. It is actually a type of electroencephalography (EEG) signal that is time-locked to a specific event or behavior. To remove artifacts from the data, this work utilizes Independent component analysis (ICA), finite impulse response (FIR) filter, and Fast Fourier Transformation (FFT) as preprocessing techniques. The Bidirectional Long Short-Term Memory (BiLSTM) network is utilized to retains the spatial relationships between the ERP data while learning changes in temporal information for a long time. This network performed well both in modeling and information extraction from the signals. To validate the model performance, the proposed framework is tested on two benchmark datasets. The proposed framework achieved state-of-the-art accuracy of 96.03% on SEED dataset and 97.31% on CAUEEG dataset for the classification tasks.

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“…Model name Average acc(%) LF-DfE-BiLSTM [7] 84.46 SRU [6] 83.63 CNN-LSTM [8] 88. 3 that compared with the recent excellent deep learning models, the model in this paper has achieved the highest average accuracy rate of 90.24%, which is 5.76%, 6.61% and 2.09% higher than the models BiLSTM, SRU and CNN-LSTM, respectively.…”

Section: Table 3 Overall Average Performance Comparison Of Each Modelmentioning

confidence: 99%

“…Literature [6] combined simple recurrent units and integrated strategies to propose an EEG emotion recognition system based on deep learning, and achieved good performance on public DEAP and SEED datasets. Literature [7] proposes emotion detection based on EEG signals based on differential entropy linear formula (LF-DfE) feature extractor and BiLSTM network classifier, but the training efficiency of BiLSTM module is low. Literature [8] proposed an EEG signal emotion recognition model based on CNN and LSTM, using one-dimensional convolution module to extract local features of EEG signal, and LSTM module to capture multi-channel fusion features, but basic models such as CNN and LSTM cannot identify The sentiment classification results affect large key features, and the recurrent network sequence feature learning process fails to distinguish the dependencies between different channels.…”

Section: Introductionmentioning

confidence: 99%

Sentiment recognition model of EEG signals combined with one-dimensional convolution and BiBASRU-AT

Zhu

Wang

Ding

et al. 2023

J. Phys.: Conf. Ser.

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To address the problems of low accuracy of EEG emotion sentiment and insufficient feature extraction ability of recurrent model, an EEG sentiment recognition model combining one-dimensional convolution and BiBASRU-AT is proposed. The data set is preprocessed in segments to expand the number of samples, and 62 channel local emotional features are extracted from one-dimensional convolution; The built-in self-attention simple recurrent unit is constructed to capture the multi-channel fusion features and the dependence between channels. The soft attention mechanism identifies the key features that have a great impact on the identification of emotional tendencies, and the linear layer outputs the positive, neutral and negative emotion recognition results. The experimental results on the EEG data set(SEED) show that the model achieves an average classification accuracy of 90.24%, which is higher than the excellent deep learning model compared with the experiment. The built-in self-attention simple recurrent unit has stronger feature capture ability, which proves the effectiveness of the model.

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Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals

Cited by 15 publications

References 34 publications

Channel Semantic Enhancement-Based Emotional Recognition Method Using SCLE-2D-CNN

Channel Semantic Enhancement-Based Emotional Recognition Method Using SCLE-2D-CNN

Prediction of mild cognitive impairment using EEG signal and BiLSTM network

Sentiment recognition model of EEG signals combined with one-dimensional convolution and BiBASRU-AT

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