Classification of EEG signals in depression by fusing temporal convolution and feature recalibration

Abstract

Aiming at the problem of insufficient feature extraction in single scale neural network model and the problem that convolutional neural network cannot process sequential tasks in the classification of EEG signals in depression, a hybrid model (BFTCNet) of dual-branch convolutional neural network (Bi_CNN) and temporal convolutional network (TCN) based on feature recalibration (FR) was proposed to classify EEG signals of depressed patients and healthy controls. Firstly, Bi_CNN module was used to extract the mixed EEG features between different frequency bands and different channels. Secondly, FR module was used to enhance the features extracted by Bi_CNN. Finally, TCN with dilated causal convolution was used for the sequence learning to capture the temporal dependency between features. In this study, 128 EEG channels of resting-state (closed-eye) EEG data from the public dataset MODMA were used as experimental data, including 29 healthy controls and 24 depression patients. The performance of the model was evaluated by the 10-fold cross validation method. The proposed BFTCNet achieves a classification accuracy of 95.98%, F1 score value of 95.47%, sensitivity and specificity of 94.21% and 97.50%, respectively. Compared with the single-scale network model EEGNet-8,2, the classification accuracy and F1 value are improved by 1.5% and 1.48%, respectively. Meanwhile, the ablation experiment proved that each sub-module had its contribution to the improvement of the model’s classification ability .

Keywords

multi-channel EEG signal / dual-branch convolutional neural network / feature recalibration / temporal convolutional network

References

[1] SMITH K. Mental health: a world of depression. Nature, 2014, 515(7526): 181.

[2] ETTMAN C K, ABDALLA S M, COHEN G H, et al. Prevalence of depression symptoms in US adults before and during the COVID-19 pandemic. JAMA Network Open, 2020, 3(9): e2019686.

[3] SAEIDI M, KARWOWSKI W, FARAHANI F V, et al. Neural decoding of EEG signals with machine learning: a systematic review. Brain Sciences, 2021, 11(11): 1525.

[4] YAHYA N, MUSA H, ONG Z Y, et al. Classification of motor functions from electroencephalogram (EEG) signals based on an integrated method comprised of common spatial pattern and wavelet transform framework. Sensors, 2019, 19(22): 4878.

[5] GRIN-YATSENKO V A, BAAS I, PONOMAREV V A, et al. Independent component approach to the analysis of EEG recordings at early stages of depressive disorders. Clinical Neurophysiology, 2010, 121(3): 281-289.

[6] RAHMAN M A, HOSSAIN M A, KABIR M R, et al. Optimization of sleep stage classification using single-channel EEG signals//2019 4th International Conference on Electrical Information and Communication Technology, December 20-22, 2019, Khulna, Bangladesh. New York: IEEE, 2020: 1-6.

[7] ACHARYA U R, OH S L, HAGIWARA Y, et al. Automated EEG-based screening of depression using deep convolutional neural network. Computer Methods and Programs in Biomedicine, 2018, 161: 103-113.

[8] UYULAN Ç, ERGÜZEL T, UNUBOL H, et al. Major depressive disorder classification based on different convolutional neural network models: deep learning approach. Clinical EEG and Neuroscience, 2020, 52: 38-51.

[9] LAWHERN V J, SOLON A J, WAYTOWICH N R, et al. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. Journal of Neural Engineering, 2018, 15(5): 056013.

[10] YAN D D, ZHAO L L, SONG X W, et al. Automated detection of clinical depression based on convolution neural network model. Biomedizinische Technik Biomedical Engineering, 2022, 67(2): 131-142.

[11] LIU B, CHANG H L, PENG K, et al. An end-to-end depression recognition method based on EEGNet. Frontiers in Psychiatry, 2022, 13: 864393.

[12] LIU W, JIA K B, WANG Z Z, et al. A depression prediction algorithm based on spatiotemporal feature of EEG signal. Brain Sciences, 2022, 12(5): 630.

[13] BAI S J, KOLTER J Z, KOLTUN V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. 2018: arXiv: 1803.01271.

[14] WANG Y X, LIU F R, YANG L J. EEG-based depression recognition using intrinsic time-scale decomposition and temporal convolution network. The Fifth International Conference on Biological Information and Biomedical Engineering, New York: ACM, 2021: 1-6.

[15] INGOLFSSON T M, HERSCHE M, WANG X Y, et al. EEG-TCNet: an accurate temporal convolutional network for embedded motor-imagery brain-machine interfaces//2020 IEEE International Conference on Systems, Man, and Cybernetics, October 11-14, 2020. Toronto, ON, Canada. New York: IEEE, 2020: 2958-2965.

[16] HU J, SHEN L, SUN G. Squeeze-and-excitation networks//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 7132-7141.

[17] CAI H S, YUAN Z Q, GAO Y W, et al. A multi-modal open dataset for mental-disorder analysis. Scientific Data, 2022, 9: 178.

[18] SEGAL D L. Diagnostic and statistical manual of mental disorders (DSM-IV-TR). The Corsini Encyclopedia of Psychology, 2010: 1-3.

[19] GRAMFORT A, LUESSI M, LARSON E, et al. MNE software for processing MEG and EEG data. NeuroImage, 2014, 86: 446-460.

[20] SUN S T, LI J X, CHEN H Y, et al. A study of resting-state EEG biomarkers for depression recognition. 2020: arXiv: 2002.11039.

[21] SEAL A, BAJPAI R, AGNIHOTRI J, et al. DeprNet: a deep convolution neural network framework for detecting depression using EEG. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 2505413.

[22] JAS M, ENGEMANN D A, BEKHTI Y, et al. Autoreject: Automated artifact rejection for MEG and EEG data. NeuroImage, 2017, 159: 417-429.

[23] MAATEN L, HINTON G. Visualizing data using t-SNE. Journal of Machine Learning Research, 2008(9): 2579-260 5.

[24] SCHIRRMEISTER R T, SPRINGENBERG J T, FIEDERER L D J, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping, 2017, 38(11): 5391-5420.

[25] ZHANG X W, LI J L, HOU K C, et al. EEG-based depression detection using convolutional neural network with demographic attention mechanism//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, July 20-24, 2020. Montreal, QC, Canada. New York: IEEE, 2020: 128-133.

[26] DENG X, FAN X F, LÜ X W, et al. SparNet: a convolutional neural network for EEG space-frequency feature learning and depression discrimination. Frontiers in Neuroinformatics, 2022, 16: 914823.

[27] MAO W D. Deep learning based research on recognition of depression patients using eeg data. Lanzhou: Lanzhou University, 2019.