Feature Analysis of EEG Based Brain-Computer Interfaces to Detect Motor Imagery

Akbar, Saima; Martinez-Enriquez, A. M.; Aslam, Muhammad; Saleem, Rabeeya

doi:10.1007/978-3-030-86993-9_45

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Therefore, RFE might not be suitable for building user-dependent models. This challenge can be solved using Recursive Feature Elimination with Cross Validation (RFECV) (Yin et al, 2017;Akbar et al, 2021;Zanetti et al, 2022), a method similar to RFE that automatically detects the optimal number of features that are required for training a model.…”

Section: Introductionmentioning

confidence: 99%

Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals

Pinilla

Voigt-Antons

García

et al. 2023

Front. Virtual Real.

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This manuscript explores the development of a technique for detecting the affective states of Virtual Reality (VR) users in real-time. The technique was tested with data from an experiment where 18 participants observed 16 videos with emotional content inside a VR home theater, while their electroencephalography (EEG) signals were recorded. Participants evaluated their affective response toward the videos in terms of a three-dimensional model of affect. Two variants of the technique were analyzed. The difference between both variants was the method used for feature selection. In the first variant, features extracted from the EEG signals were selected using Linear Mixed-Effects (LME) models. In the second variant, features were selected using Recursive Feature Elimination with Cross Validation (RFECV). Random forest was used in both variants to build the classification models. Accuracy, precision, recall and F1 scores were obtained by cross-validation. An ANOVA was conducted to compare the accuracy of the models built in each variant. The results indicate that the feature selection method does not have a significant effect on the accuracy of the classification models. Therefore, both variations (LME and RFECV) seem equally reliable for detecting affective states of VR users. The mean accuracy of the classification models was between 87% and 93%.

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Section: Introductionmentioning

confidence: 99%

Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals

Pinilla

Voigt-Antons

García

et al. 2023

Front. Virtual Real.

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show abstract

“…The method can achieve feature dimensionality reduction in EEG signals in a short time, has low algorithm complexity, and can extract higher signal features in MI-EEG. Akbar et al [ 11 ] analyzed the potential features of MI-based C3, C4, and Cz EEG signals using low-pass filter banks with five different frequency banks between 0 and 5 Hz. Digital Butterworth low-pass filter banks with cut-off frequencies of 0.4, 1, and 2 Hz were illustrated, and the identification of neural activities from MI-EEG signals was achieved.…”

Section: Introductionmentioning

confidence: 99%

IMF-Based MF and HS Energy Feature Information of F5, and F6 Movement and Motor Imagery EEG Signals in Delta Rhythms Using HHT

Lin

2023

Sensors

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This study aims to extract the energy feature distributions in the form of marginal frequency (MF) and Hilbert spectrum (HS) in the intrinsic mode functions (IMF) domain for actual movement (AM)-based and motor imagery (MI)-based electroencephalogram (EEG) signals using the Hilbert–Huang transformation (HHT) time frequency (TF) analysis method. Accordingly, F5 and F6 EEG signal TF energy feature distributions in delta (0.5–4 Hz) rhythm are explored. We propose IMF-based and residue function (RF)-based MF and HS feature information extraction methods with IMFRFERDD (IMFRF energy refereed distribution density), IMFRFMFERDD (IMFRF MF energy refereed distribution density), and IMFRFHSERDD (IMFRF HS energy refereed distribution density) parameters using HHT with application to AM, MI EEG F5, and F6 signals in delta rhythm. The AM and MI tasks involve simultaneously opening fists and feet, as well as simultaneously closing fists and feet. Eight samples (32 in total) with a time duration of 1000 ms are extracted for analyzing F5AM, F5MI, F6AM, and F6MI EEG signals, which are decomposed into five IMFs and one RF. The maximum average IMFRFERDD values of IMF4 are 3.70, 3.43, 3.65, and 3.69 for F5AM, F5MI, F6 AM, and F6MI, respectively. The maximum average IMFRFMFERDD values of IMF4 in the delta rhythm are 21.50, 20.15, 21.02, and 17.30, for F5AM, F5MI, F6AM, and F6MI, respectively. Additionally, the maximum average IMFRFHSERDD values of IMF4 in delta rhythm are 39,21, 39.14, 36.29, and 33.06 with time intervals of 500–600, 800–900, 800–900, and 500–600 ms, for F5AM, F5MI, F6AM, and F6MI, respectively. The results of this study, advance our understanding of meaningful feature information of F5MM, F5MI, F6MM, and F6MI, enabling the design of MI-based brain-computer interface assistive devices for disabled persons.

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Feature Analysis of EEG Based Brain-Computer Interfaces to Detect Motor Imagery

Cited by 2 publications

References 13 publications

Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals

Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals

IMF-Based MF and HS Energy Feature Information of F5, and F6 Movement and Motor Imagery EEG Signals in Delta Rhythms Using HHT

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