An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets

Khare, Smith K.; Gaikwad, Nikhil B.; Bokde, Neeraj Dhanraj

doi:10.3390/s22218128

Cited by 7 publications

(3 citation statements)

References 59 publications

(72 reference statements)

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“…The method involves using the TQWT technique with automatically selected tuning parameters and then selecting important features from the resulting signals using a least squares SVM classifier. The proposed method achieved high accuracy of 99.78%, which is superior to other state-of-the-art techniques using the same database [61]. Maksimenko et al found that a combination of delta and mu/alpha frequency bands in EEG signals can be used to extract features of brain activity associated with motor execution and MI in untrained individuals.…”

Section: Nonlinear Dynamical Studies On Motor-imagerymentioning

confidence: 95%

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Kargarnovin,

Hernandez,

Farahani

et al. 2023

Brain Sciences

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(1) Background: Chaos, a feature of nonlinear dynamical systems, is well suited for exploring biological time series, such as heart rates, respiratory records, and particularly electroencephalograms. The primary purpose of this article is to review recent studies using chaos theory and nonlinear dynamical methods to analyze human performance in different brain processes. (2) Methods: Several studies have examined chaos theory and related analytical tools for describing brain dynamics. The present study provides an in-depth analysis of the computational methods that have been proposed to uncover brain dynamics. (3) Results: The evidence from 55 articles suggests that cognitive function is more frequently assessed than other brain functions in studies using chaos theory. The most frequently used techniques for analyzing chaos include the correlation dimension and fractal analysis. Approximate, Kolmogorov and sample entropy account for the largest proportion of entropy algorithms in the reviewed studies. (4) Conclusions: This review provides insights into the notion of the brain as a chaotic system and the successful use of nonlinear methods in neuroscience studies. Additional studies of brain dynamics would aid in improving our understanding of human cognitive performance.

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Section: Nonlinear Dynamical Studies On Motor-imagerymentioning

confidence: 95%

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Kargarnovin,

Hernandez,

Farahani

et al. 2023

Brain Sciences

View full text Add to dashboard Cite

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“…Among BCI modalities, electroencephalography (EEG) is frequently used because of its high temporal resolution, noninvasiveness, and portability. EEG signals have shown an excellent ability to classify different motor conditions [ 5 ] and different motions during MI tasks [ 6 , 7 ]. In the former studies of EEG, many investigations have been focused on static features, which may lose important temporal information.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study

Wang

Chen

2023

Bioengineering

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Motor imagery-based brain–computer interfaces (BCI) have been widely recognized as beneficial tools for rehabilitation applications. Moreover, visually guided motor imagery was introduced to improve the rehabilitation impact. However, the reported results to support these techniques remain unsatisfactory. Electroencephalography (EEG) signals can be represented by a sequence of a limited number of topographies (microstates). To explore the dynamic brain activation patterns, we conducted EEG microstate and microstate-specific functional connectivity analyses on EEG data under motor imagery (MI), motor execution (ME), and guided MI (GMI) conditions. By comparing sixteen microstate parameters, the brain activation patterns induced by GMI show more similarities to ME than MI from a microstate perspective. The mean duration and duration of microstate four are proposed as biomarkers to evaluate motor condition. A support vector machine (SVM) classifier trained with microstate parameters achieved average accuracies of 80.27% and 66.30% for ME versus MI and GMI classification, respectively. Further, functional connectivity patterns showed a strong relationship with microstates. Key node analysis shows clear switching of key node distribution between brain areas among different microstates. The neural mechanism of the switching pattern is discussed. While microstate analysis indicates similar brain dynamics between GMI and ME, graph theory-based microstate-specific functional connectivity analysis implies that visual guidance may reduce the functional integration of the brain network during MI. Thus, we proposed that combined MI and GMI for BCI can improve neurorehabilitation effects. The present findings provide insights for understanding the neural mechanism of microstates, the role of visual guidance in MI tasks, and the experimental basis for developing new BCI-aided rehabilitation systems.

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“…Similar attempts have been made in the study of EEG (electroencephalogram) signals since there are many diferent waves in the EEG (mainly consisting of α, β, c, δ,, and θ), and the application of EEG signals to solve some practical problems requires the analysis of diferent waves: the literature [22] developed a scheme to automatically identify schizophrenia by decomposing the EEG signal through EMD (empirical mode decomposition) and calculating 22 each feature from it; Reference [23] proposed a computer-aided clinical decision support system (CACDSS) to detect and diagnose Parkinson's disease through EEG by combining automatic variational modal decomposition (AOVMD) and automatic extreme learning machine (AOELM) classifers; the literature [24] developed an EEG rhythm separation (VHERS) based on variational modal decomposition (VMD) and Hilbert transform (HT) to help experts detect attention defcit hyperactivity disorder (ADHD) in a real-time situation. Reference [25] proposed the robust tuneable Q wavelet transform (TQWT) for the automatic selection of optimal tuning parameters to accurately decompose nonsmooth EEG signals and identify motor imagery (MI) tasks with low complexity.…”

Section: Introductionmentioning

confidence: 99%

Speech Deception Detection Based on EMD and Temporal Neural Network

Jiang

Chen

Yuan

et al. 2023

Computational Intelligence and Neuroscience

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Deceptive behaviour is a common phenomenon in human society. Research has shown that humans are not good at distinguishing deception, so studying automated deception detection techniques is a critical task. Most of the relevant technologies are susceptible to personal and environmental influences: EEG-based technologies need large and expensive equipment, facial-based technologies are sensitive with the camera’s perspective, and these reasons have somewhat limited the development of applications for deception detection technologies. In contrast, the equipment required for speech deception detection is cheap and easy to use, and the capture of speech is highly covert. Based on the application of signal decomposition algorithms in other fields such as EEG signals and speech emotion recognition, this paper proposed a signal decomposition and reconstruction method based on EMD to process the speech signal and a better deception detection performance was obtained by improving the speech quality. The comparison results with other decomposition algorithms showed that the EMD decomposition algorithm is the most suitable for our method. Across many different classification algorithms, accuracy improved by an average of 2.05% and the F1 score improved by an average of 1.7%. In addition, a new deception detector, called the TCN-LSTM network, was proposed in this paper. Experiments showed that this network organically combines the processing capability of TCN and LSTM for time series data; the recognition rate of deception detection was greatly improved, with the highest accuracy and F1 score reaching 86.2% and 86.0% under the EMD-based signal decomposition reconstruction method. Based on the research in this paper, the signal decomposition algorithms need to be further optimised for speech signals and more classification algorithms not used for this task should be tried.

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An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets

Cited by 7 publications

References 59 publications

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study

Speech Deception Detection Based on EMD and Temporal Neural Network

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