Applying ICA in EEG: Choice of the Window Length and of the Decorrelation Method

Korāts, Gundars; Cam, Steven Le; Ranta, Radu; Hamid, Mohamed

doi:10.1007/978-3-642-38256-7_18

Cited by 18 publications

(14 citation statements)

References 15 publications

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“…Early studies including [2][3][4][5][6][7][8] utilized handpicked features and applied various machine learning techniques such as support vector machine and principal component analysis (PCA) to the features. While these studies show viable accuracy, performances are restricted from feature selection.…”

Section: Early Modelsmentioning

confidence: 99%

Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach

Yang

Han

Min

2019

Sensors

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Visual stimuli from photographs and artworks raise corresponding emotional responses. It is a long process to prove whether the emotions that arise from photographs and artworks are different or not. We answer this question by employing electroencephalogram (EEG)-based biosignals and a deep convolutional neural network (CNN)-based emotion recognition model. We employ Russell’s emotion model, which matches emotion keywords such as happy, calm or sad to a coordinate system whose axes are valence and arousal, respectively. We collect photographs and artwork images that match the emotion keywords and build eighteen one-minute video clips for nine emotion keywords for photographs and artwork. We hired forty subjects and executed tests about the emotional responses from the video clips. From the t-test on the results, we concluded that the valence shows difference, while the arousal does not.

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Section: Early Modelsmentioning

confidence: 99%

Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach

Yang

Han

Min

2019

Sensors

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“…They can be separated by the https://www.degruyter.com/view/j/cjece ICA algorithm to extract useful information on EEG signals. The design has been perceived as an impressive mechanism for constrict artifacts and determine mathematical independent cortical measure in the scalp and intracranial EEG reporting [10] meanwhile artifact rejection discards corrupted data tranches, thereby conserving the original bulk of samples, giving in a superior signal-to-noise ratio for consecutive search steps. ICA performs better than PCA when the volume of the data to disintegrate is giant [5].…”

Section: A Independent Component Analysismentioning

confidence: 99%

“…PCA is one of the most meaningful dimensionality reduction technology by using the statistical method. It makes usage of scientific procedures that apply an orthogonal transformation to alter a series of measurements of interacted vectors into a series of linearly disrelated vectors called principal components [10]. The dominance of PCA is that it takes care of the reduction of the dimension of the factor vectors and the complexity of signal feature extraction and classification.…”

Section: B Principal Component Analysismentioning

confidence: 99%

A Review of Processing Methods and Classification Algorithm for EEG Signal

Xie

Oniga

2020

Carpathian Journal of Electronic and Computer Engineering

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The analysis technique of EEG signals is developing promptly with the evolution of Brain Computer- Interfaces science. The processing and classification algorithm of EEG signals includes three states: pre-processing, feature extraction and classification. The article discusses both conventional and recent processing techniques of EEG signals at the phases of preprocessing, feature extraction and classification. Finally, analyze popular research directions in the future.

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“…The electrical activities of the cortex were propagated through the anatomical structures to the scalp. Therefore, the acquired EEG was a mixture of the source signals from different brain regions, which carried a great deal of redundant information with a low SNR (signal to noise ratio) (Korats et al, 2012). Additionally, the asymmetry features regarding brain regions, such as DASM (differential asymmetry), RASM (rational asymmetry) and DCAU (differential causality) have been explored in the literature on emotion recognition (Zheng et al, 2016; Li et al, 2018), indicating that the spatial information of EEG signals is useful.…”

Section: Introductionmentioning

confidence: 99%

SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG

Xing

et al. 2019

Front. Neurorobot.

206

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EEG-based automatic emotion recognition can help brain-inspired robots in improving their interactions with humans. This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG). The framework consists of a linear EEG mixing model and an emotion timing model. Our proposed framework considerably decomposes the EEG source signals from the collected EEG signals and improves classification accuracy by using the context correlations of the EEG feature sequences. Specially, Stack AutoEncoder (SAE) is used to build and solve the linear EEG mixing model and the emotion timing model is based on the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The framework was implemented on the DEAP dataset for an emotion recognition experiment, where the mean accuracy of emotion recognition achieved 81.10% in valence and 74.38% in arousal, and the effectiveness of our framework was verified. Our framework exhibited a better performance in emotion recognition using multi-channel EEG than the compared conventional approaches in the experiments.

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Applying ICA in EEG: Choice of the Window Length and of the Decorrelation Method

Cited by 18 publications

References 15 publications

Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach

Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach

A Review of Processing Methods and Classification Algorithm for EEG Signal

SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG

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