EEG-Based Emotion Classification Using Spiking Neural Networks

Luo, Yuling; Fu, Qiang; Xie, Juntao; Qin, Yunbai; Wu, Guopei; Liu, Junxiu; Jiang, Frank; Cao, Yi; Ding, Xuemei

doi:10.1109/access.2020.2978163

Cited by 102 publications

(33 citation statements)

References 42 publications

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“…For EEG signals with different characteristics, commonly used classifiers include Fisher discriminant classifier, support vector machine, neural network algorithm, etc. ( Rozza et al, 2012 ; Rubén et al, 2019 ; Dib et al, 2020 ; Ghonchi et al, 2020 ; Luo et al, 2020 ; Srirangan et al, 2020 ).…”

Section: Mi-bci System Structurementioning

confidence: 99%

A Novel Smart Motor Imagery Intention Human-Computer Interaction Model Using Extreme Learning Machine and EEG Signals

Lee

2021

Front. Neurosci.

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The brain is the central nervous system that governs human activities. However, in modern society, more and more diseases threaten the health of the brain and nerves and spinal cord, making the human brain unable to conduct normal information interaction with the outside world. The rehabilitation training of the brain-computer interface can promote the nerve repair of the sensorimotor cortex in patients with brain diseases. Therefore, the research of brain-computer interface for motor imaging is of great significance for patients with brain diseases to restore motor function. Due to the characteristics of non-stationary, nonlinear, and individual differences of EEG signals, there are still many difficulties in the analysis and classification of EEG signals at this stage. In this study, the Extreme Learning Machine (ELM) model was used to classify motor-imaging EEG signals, identify the user’s intention, and control external devices. Considering that single-modal features cannot represent the core information, this study uses a fusion feature that combines temporal and spatial features as the final feature data. The fusion features are input to the trained ELM classifier, and the final classification result is obtained. Two sets of BCI competition data in the BCI competition public database are used to verify the validity of the model. The experimental results show that the ELM model has achieved a classification accuracy of 0.7832 in the classification task of Data Sets IIb, which is higher than other comparison algorithms, and shows universal applicability among different subjects. In addition, the average recognition rate of this model in the Data Sets IIIa classification task reaches 0.8347, which has obvious advantages compared with the comparative classification algorithm. The classification effect is smaller than the classification effect obtained by the champion algorithm of the same project, which has certain reference value.

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Section: Mi-bci System Structurementioning

confidence: 99%

A Novel Smart Motor Imagery Intention Human-Computer Interaction Model Using Extreme Learning Machine and EEG Signals

Lee

2021

Front. Neurosci.

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“…Samples with number of subjects below this threshold were considered not statistically significant. Studies claiming the best accuracy on emotional valence assessment are based on public EEG signal datasets: SEED [24][25][26][27][28][29] , DEAP [25][26][27][28][30][31][32][33][34][35][36][37][38][39][40] , and DREAMER 29,37,38 .…”

Section: Related Workmentioning

confidence: 99%

High-wearable EEG-Based Detection of Emotional Valence for Scientific Measurement of Emotions

Apicella

Arpaïa

Mastrati

et al. 2021

Preprint

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An emotional-valence detection method for a very–high wearable EEG-based system is proposed. Valence detection occurs along the interval scale theorized by the circumplex model of emotions. The binary choice, positive valence vs negative valence, represents a first step towards the adoption of a metric scale with a finer resolution. Wearability is guaranteed by a wireless cap with conductive-rubber dry electrodes and 8 data acquisition channels. Experimental validation was realized on 25 volunteers without depressive disorders. The metrological reference was built by combining the rating of a standardized set of pictures from dataset Oasis and results from the Self Assessment Manikin questionnaire. Two different strategies for feature extraction were compared: (i) based on a-priory knowledge (i.e. Hemispheric Asymmetry Theories) and (ii) automated. A pipeline of a custom 12-band Filter Bank and Common Spatial Pattern algorithm is the method proposed for automated feature extraction. Four machine learning classifiers were tested for validating the proposed method in discriminating two classes (high or low emotional valence). An intra-individual average accuracy, 96.2 %, was obtained by a shallow artificial neural network, while K-Nearest Neighbors allowed obtaining 80.3 % of inter-individual accuracy. Considering the wearability, and the well-founded metrological reference, results are the state of the art in emotional valence detection.

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“…Their method achieves a recognition accuracy 87.44% and 88.49% for valence and arousal classes, respectively. Luo et al [33] proposed a novel method of using the spiking neural networks (SNNs) and the electroencephalograph (EEG) processing techniques using two handcrafted features, e.g., FFT and DWT, to recognize emotion states. Their experimental results showed that, by using the variance data processing technique and SNN, the emotion states of arousal, valence, dominance and liking can be classified with accuracies of 74%, 78%, 80%, and 86.27% for the DEAP dataset, and an overall accuracy is 96.67% for the SJTU Emotion EEG Dataset (SEED) dataset [34].…”

Section: Related Studiesmentioning

confidence: 99%

Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network

Cho

Hwang

2020

Sensors

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Emotion recognition plays an important role in the field of human–computer interaction (HCI). An electroencephalogram (EEG) is widely used to estimate human emotion owing to its convenience and mobility. Deep neural network (DNN) approaches using an EEG for emotion recognition have recently shown remarkable improvement in terms of their recognition accuracy. However, most studies in this field still require a separate process for extracting handcrafted features despite the ability of a DNN to extract meaningful features by itself. In this paper, we propose a novel method for recognizing an emotion based on the use of three-dimensional convolutional neural networks (3D CNNs), with an efficient representation of the spatio-temporal representations of EEG signals. First, we spatially reconstruct raw EEG signals represented as stacks of one-dimensional (1D) time series data to two-dimensional (2D) EEG frames according to the original electrode position. We then represent a 3D EEG stream by concatenating the 2D EEG frames to the time axis. These 3D reconstructions of the raw EEG signals can be efficiently combined with 3D CNNs, which have shown a remarkable feature representation from spatio-temporal data. Herein, we demonstrate the accuracy of the emotional classification of the proposed method through extensive experiments on the DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) dataset. Experimental results show that the proposed method achieves a classification accuracy of 99.11%, 99.74%, and 99.73% in the binary classification of valence and arousal, and, in four-class classification, respectively. We investigate the spatio-temporal effectiveness of the proposed method by comparing it to several types of input methods with 2D/3D CNN. We then verify the best performing shape of both the kernel and input data experimentally. We verify that an efficient representation of an EEG and a network that fully takes advantage of the data characteristics can outperform methods that apply handcrafted features.

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EEG-Based Emotion Classification Using Spiking Neural Networks

Cited by 102 publications

References 42 publications

A Novel Smart Motor Imagery Intention Human-Computer Interaction Model Using Extreme Learning Machine and EEG Signals

A Novel Smart Motor Imagery Intention Human-Computer Interaction Model Using Extreme Learning Machine and EEG Signals

High-wearable EEG-Based Detection of Emotional Valence for Scientific Measurement of Emotions

Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network

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