SEER-net: Simple EEG-based Recognition network. (May 2023)

• Record Type: Journal Article
• Authors: Kuang, Dongyang; Michoski, Craig
• Is Part Of: Biomedical signal processing and control. Volume 83(2023)
• Publication Date: 2023-05
• Subjects: Brain–computer interface (BCI) -- EEG -- Emotion recognition -- Deep learning -- Neural networks; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2023.104620
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: This paper presents a Simple ElectroEncephalographic-based Recognition Network (SEER-net) utilizing normalized EEG signals directly as input for the purpose of end-to-end classification tasks. SEER-net utilizes a forked design with kernels in each fork tailored to operate along different directions on extracted features from temporal convolution over inputs. With this design, SEER-net is able to train with significantly fewer parameters than comparable networks while preserving classification accuracy comparable to state-of-the-art. In our experiments, the proposed SEER-net achieves 90.73% mean test accuracy with 3485 parameters in the subject-dependent emotion classification tasks on SEED dataset. Additional ablation studies are also performed in order to compare the effects three variations within the SEER-net architecture have on performance. These include: (1) comparing the forked design against the single branch approaches, (2) investigating changes in the prediction accuracy when inputs are differently filtered, and (3) comparing SEER-net's performance when a wavelet kernel is used for the first temporal convolution. Finally, several visualization techniques are adopted to explore different representations and patterns present within the trained SEER-net in order to extract deeper physical insight and potential human-relatable interpretations. Highlights: A novel compact model achieving accuracy comparable to state-of-the-art. An end-to-end model requiring minimal preprocessing and feature engineering. Two different directions of convolutions for better feature exploitation. Extensive investigations on trained model's behavior and feature attention patterns.