Hybrid Method of Automated EEG Signals’ Selection Using Reversed Correlation Algorithm for Improved Classification of Emotions

Wosiak, Agnieszka; Dura, Aleksandra

doi:10.3390/s20247083

Cited by 8 publications

(5 citation statements)

References 79 publications

(103 reference statements)

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“…In [135], the RCA was adapted for the channel selection task. The intuition is that a lower correlation between channels can be interpreted as low connectivity with the other ones.…”

Section: Device Positionsmentioning

confidence: 99%

A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels

et al. 2022

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The market uptake of Brain-Computer Interface technologies for clinical and non-clinical applications is attracting the scientific world towards the development of daily-life wearable systems. Beyond the use of dry electrodes and wireless technology, reducing the number of channels is crucial to enhance the ergonomics of devices. This paper presents a review of the studies exploiting a number of channels less than 16 for electroencephalographic (EEG) based-emotion recognition. The main findings of this review concern: (i) the criteria to select the most promising scalp areas for EEG acquisitions; (ii) the attention to prior neurophysiological knowledge; and (iii) the convergences among different studies with respect to preferable areas of the scalp for signal acquisition. Three main approaches emerge for channel selection: data-driven, prior knowledge-based, and based on commercially-available wearable solutions. The most spread is the data-driven, but the neurophysiology of emotions is rarely taken into account. Furthermore, commercial EEG devices usually do not provide electrodes purposefully chosen to assess emotions. Considerable convergences emerge for some electrodes: Fp1, Fp2, F3 and F4 resulted the most informative channels for the valence dimension, according to both data-driven and neurophysiological prior knowledge approaches. The P3 and P4 resulted in being significant for the arousal dimension.INDEX TERMS Emotion, EEG, channel selection, machine learning, neurophysiology of emotions, wearable devices.

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“…In [135], the RCA was adapted for the channel selection task. The intuition is that a lower correlation between channels can be interpreted as low connectivity with the other ones.…”

Section: Device Positionsmentioning

confidence: 99%

A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels

et al. 2022

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“…Lots of researchers use multi-channel EEG signals (usually 32-channel, 62-channel, or more channels' EEG signals of the entire brain) for emotion recognition to improve classification accuracy (Yu and Yu, 2021 ). However, many EEG channels contain noise or redundancy, which is detrimental to emotion recognition in practice (Wosiak and Dura, 2020 ; Al-Saegh et al, 2021 ). Furthermore, the large number of EEG channels makes data acquisition difficult and increases the computational complexity of data processing.…”

Section: Introductionmentioning

confidence: 99%

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems

Liu,

2023

Front. Neurosci.

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BackgroundFor non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem.MethodsThe multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space.ResultsThe proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels.ConclusionThe formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.

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“…The selection of EEG channels is challenging due to the interconnection between the electrodes [7]. The purposes of channel selection are dimensionality reduction, faster processing, performance improvement of the classi cation model, identi cation of the area of the brain that is most active in an intended event or task, and reduction of electrodes setup times.…”

Section: Introductionmentioning

confidence: 99%

“…The channel reduction procedure can reduce power consumption and computational time by reducing data size. In addition, due to the interest of the medical industry and community in portable monitoring systems for patients, channel selection leads to the implementation of low-cost portable headsets for pain monitoring.The selection of EEG channels is challenging due to the interconnection between the electrodes [7]. The purposes of channel selection are dimensionality reduction, faster processing, performance improvement of the classi cation model, identi cation of the area of the brain that is most active in an intended event or task, and reduction of electrodes setup times.…”

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A Novel Channel Selection Approach for Human Neonate’s Pain EEG Data Analysis

Talebi

Frounchi

Tazehkand

2022

Preprint

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Quantitative measurement of pain using the Electroencephalogram (EEG) signals has received much attention, recently. Pain EEG data processing is associated with complexity and high computational cost. This study aims to propose a new method for selecting efficient EEG channels to determine the area of the scalp that contains the most information about brain activity during acute pain in neonates. Also, selecting relevant channels in pain assessment reduces computational costs. In this study, a new channel selection approach is proposed, which is a combination of filter and wrapper methods. A new pseudo-Sequential Forward Feature Selection (pseudo-SFFS) method is presented to reduce the computational complexity of wrapper methods. We preprocessed data by applying a bandpass filter. We used wavelet transform to extract features. After extracting the features, we applied two feature selection steps. In the first step, we applied the T-test to the extracted features. In the second step, we selected the effective channels based on the output of the applied pseudo-SFFS algorithm into Support Vector Machine (SVM), Decision Tree (DT), and Gaussian Naive Bayesian (GNB) classifiers. Using the proposed method two channels of the sensorimotor cortex including Cz and C4 channels have been selected from 18 EEG channels for pain stimulation through the left heel of neonates. Also, the results show that most of the acute pain information of neonates is related to the delta and theta frequency bands.

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Hybrid Method of Automated EEG Signals’ Selection Using Reversed Correlation Algorithm for Improved Classification of Emotions

Cited by 8 publications

References 79 publications

A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels

A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems

A Novel Channel Selection Approach for Human Neonate’s Pain EEG Data Analysis

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