ScalingNet: Extracting features from raw EEG data for emotion recognition

Jingzhao Hu · Chen Wang · Qiaomei Jia · Qirong Bu · Richard F. E. Sutcliffe · Jun Hong Feng

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Wireless Signal Modulation Classification

Abstract

Convolutional Neural Networks (CNNs) have achieved remarkable performance breakthroughs in a variety of tasks. Recently, CNN-based methods that are fed with hand-extracted EEG features have steadily improved their performance on the emotion recognition task. In this paper, we propose a novel convolutional layer, called the Scaling Layer, which can adaptively extract effective data-driven spectrogram-like features from raw EEG signals. Furthermore, it exploits convolutional kernels scaled from one data-driven pattern to exposed a frequency-like dimension to address the shortcomings of prior methods requiring hand-extracted features or their approximations. ScalingNet, the proposed neural network architecture based on the Scaling Layer, has achieved state-of-the-art results across the established DEAP and AMIGOS benchmark datasets.

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References

2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 1999Core affect, prototypical emotional episodes, and other things called emotion: Dissecting the elephant.2018EmotionMeter: A Multimodal Framework for Recognizing Human Emotions 2018AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups 2019EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations 2018Emotion Recognition from Multi-Channel EEG through Parallel Convolutional Recurrent Neural Network 2016Emotion recognition from multi-channel EEG data through Convolutional Recurrent Neural Network 2016Emotion Recognition Using Multimodal Deep Learning

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2025Identifying Stable EEG Patterns in Manipulation Task for Negative Emotion Recognition 2024Human emotion recognition using Machine learning techniques based on the physiological signal 2022Emotion Recognition of Subjects With Hearing Impairment Based on Fusion of Facial Expression and EEG Topographic Map

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