Electroencephalogram Based Reaction Time Prediction With Differential Phase Synchrony Representations Using Co-Operative Multi-Task Deep Neural Networks

Reddy, Tharun Kumar; Arora, Vipul; Kumar, Satyam; Behera, Laxmidhar; Wang, Yukai; Lin, Chin‐Teng

doi:10.1109/tetci.2018.2881229

Cited by 28 publications

(6 citation statements)

References 39 publications

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“…Indeed, it is still unclear whether DL methods provide consistent performance improvements over traditional ML approaches for EEG data [36,44]. Furthermore, despite recent studies implementing nested cross-validation (nCV) for hyperparameter (HP) optimization [45,46], robust consideration of HP selection in DL-EEG studies has been severely lacking in the literature, with almost 80% not mentioning HP searching at all [36]. Of the 21% of all DL EEG studies which considered HP optimization, the majority applied trial and error or grid search.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

Cooney

Korik

Folli

et al. 2020

Sensors

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Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain–computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across several domains. However, it remains an open question whether DL methods provide significant advances over traditional machine learning (ML) approaches for classification of imagined speech. Furthermore, hyperparameter (HP) optimization has been neglected in DL-EEG studies, resulting in the significance of its effects remaining uncertain. In this study, we aim to improve classification of imagined speech EEG by employing DL methods while also statistically evaluating the impact of HP optimization on classifier performance. We trained three distinct convolutional neural networks (CNN) on imagined speech EEG using a nested cross-validation approach to HP optimization. Each of the CNNs evaluated was designed specifically for EEG decoding. An imagined speech EEG dataset consisting of both words and vowels facilitated training on both sets independently. CNN results were compared with three benchmark ML methods: Support Vector Machine, Random Forest and regularized Linear Discriminant Analysis. Intra- and inter-subject methods of HP optimization were tested and the effects of HPs statistically analyzed. Accuracies obtained by the CNNs were significantly greater than the benchmark methods when trained on both datasets (words: 24.97%, p < 1 × 10–7, chance: 16.67%; vowels: 30.00%, p < 1 × 10–7, chance: 20%). The effects of varying HP values, and interactions between HPs and the CNNs were both statistically significant. The results of HP optimization demonstrate how critical it is for training CNNs to decode imagined speech.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

Cooney

Korik

Folli

et al. 2020

Sensors

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“…The multilayer perceptron neural networks (MLP) with stochastic gradient descent algorithm was utilize in [27] to recognize the eye state. Researchers in [28] proposed various algorithms to improve the convergence speed and classification accuracy with neural networks, while many deep learning based approaches have also been suggested in BCI with driver drowsiness detection applications [29].…”

Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

et al. 2019

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The robustness and computational load are the key challenges in motor imagery (MI) based on electroencephalography (EEG) signals to decode for the development of practical brain-computer interface (BCI) systems. In this study, we propose a robust and simple automated multivariate empirical wavelet transform (MEWT) algorithm for the decoding of different MI tasks. The main contributions of this study are four-fold. First, the multiscale principal component analysis method is utilized in the preprocessing module to obtain robustness against noise. Second, a novel automated channel selection strategy is proposed and then is further verified with comprehensive comparisons among three different strategies for decoding channel combination selection. Third, a sub-band alignment method by utilizing MEWT is adopted to obtain joint instantaneous amplitude and frequency components for the first time in MI applications. Four, a robust correlation-based feature selection strategy is applied to largely reduce the system complexity and computational load. Extensive experiments for subject-specific and subject independent cases are conducted with the three-benchmark datasets from BCI competition III to evaluate the performances of the proposed method by employing typical machine-learning classifiers. For subject-specific case, experimental results show that an average sensitivity, specificity and classification accuracy of 98% was achieved by employing multilayer perceptron neural networks, logistic model tree and least-square support vector machine (LS-SVM) classifiers, respectively for three datasets, resulting in an improvement of upto 23.50% in classification accuracy as compared with other existing method. While an average sensitivity, specificity and classification accuracy of 93%, 92.1% and 91.4% was achieved for subject independent case by employing LS-SVM classifier for all datasets with an increase of up to 18.14% relative to other existing methods. Results also show that our proposed algorithm provides a classification accuracy of 100% for subjects with small training size in subject-specific case, and for subject independent case by employing a single source subject. Such satisfactory results demonstrate the great potential of the proposed MEWT algorithm for practical MI EEG signals classification.

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“…Therefore, one needs to clean the raw EEG to better suit the requirements. To achieve this, a variety of pre-processing methods can be applied [42], [43], [44], [45], [46], including:…”

Section: Bci Techniques and Algorithms A Pre-processing Strategiesmentioning

confidence: 99%

Virtual Reality Cognitive Gaming Based on Brain Computer Interfacing: A Narrative Review

et al. 2023

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The present article explores the most popular approaches and the best practices for the design and implementation of cognitive gaming interventions that combine Brain Computer Interface (BCI) systems with Virtual Reality (VR). We focus on interventions that target cognitive skills related to perception, visuospatial attention and visuospatial memory. To this purpose, we review the techniques and algorithms that are commonly used for data pre-processing, feature extraction, and classification in such interventions. We discuss issues related to BCI-VR Cognitive Gaming, including the BCI paradigms, the action tasks and environments, user characteristics, algorithms, channels, accuracy, and the most prominent findings. We conclude with a discussion of the current challenges, limitations, future research directions, and the potential commercial applications of BCI-VR in cognitive gaming.INDEX TERMS Brain computer interface (BCI), cognition, electroencephalography (EEG), gaming, virtual reality (VR).

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Electroencephalogram Based Reaction Time Prediction With Differential Phase Synchrony Representations Using Co-Operative Multi-Task Deep Neural Networks

Cited by 28 publications

References 39 publications

Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

Virtual Reality Cognitive Gaming Based on Brain Computer Interfacing: A Narrative Review

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