Neuro-evolutionary approach for optimal selection of EEG channels in motor imagery based BCI application

Idowu, Oluwagbenga Paul; Adelopo, Oluwaremilekun; Ilesanmi, Ademola Enitan; Li, Xiangxin; Samuel, Oluwarotimi Williams; Peng, Fang; Li, Guanglin

doi:10.1016/j.bspc.2021.102621

Cited by 29 publications

(14 citation statements)

References 35 publications

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“…With the proper cognitive experimental design, researchers have introduced deep learning algorithms that can predict future brain states from pre-event sensory data. In Idowu et al (2021), the authors introduced an LSTM-SAE model to predict the future motor intention of users undergoing visual stimuli. However, neuroscience problems are inherently multimodal, relying on unimodal information could skew results (Abrol et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Multimodal explainable AI predicts upcoming speech behavior in adults who stutter

et al. 2022

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A key goal of cognitive neuroscience is to better understand how dynamic brain activity relates to behavior. Such dynamics, in terms of spatial and temporal patterns of brain activity, are directly measured with neurophysiological methods such as EEG, but can also be indirectly expressed by the body. Autonomic nervous system activity is the best-known example, but, muscles in the eyes and face can also index brain activity. Mostly parallel lines of artificial intelligence research show that EEG and facial muscles both encode information about emotion, pain, attention, and social interactions, among other topics. In this study, we examined adults who stutter (AWS) to understand the relations between dynamic brain and facial muscle activity and predictions about future behavior (fluent or stuttered speech). AWS can provide insight into brain-behavior dynamics because they naturally fluctuate between episodes of fluent and stuttered speech behavior. We focused on the period when speech preparation occurs, and used EEG and facial muscle activity measured from video to predict whether the upcoming speech would be fluent or stuttered. An explainable self-supervised multimodal architecture learned the temporal dynamics of both EEG and facial muscle movements during speech preparation in AWS, and predicted fluent or stuttered speech at 80.8% accuracy (chance=50%). Specific EEG and facial muscle signals distinguished fluent and stuttered trials, and systematically varied from early to late speech preparation time periods. The self-supervised architecture successfully identified multimodal activity that predicted upcoming behavior on a trial-by-trial basis. This approach could be applied to understanding the neural mechanisms driving variable behavior and symptoms in a wide range of neurological and psychiatric disorders. The combination of direct measures of neural activity and simple video data may be applied to developing technologies that estimate brain state from subtle bodily signals.

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Section: Introductionmentioning

confidence: 99%

Multimodal explainable AI predicts upcoming speech behavior in adults who stutter

et al. 2022

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“…The EEG channel selection algorithm (e.g., XCDC, Neuroevolutionary approach, automatic channel selection, and squeeze and excitation blocks (ACS-SE)) is a solid baseline when combined with deep neural networks (e.g., CNN, multi-layer perceptron neural network (MLP-NN), etc.) classifier [ 21 , 22 , 23 ]. When comparing many EEG channel selection algorithms with various classifiers, it was discovered that DNN and support vector machine (SVM) classifiers produce the best results.…”

Section: Introductionmentioning

confidence: 99%

“…When comparing many EEG channel selection algorithms with various classifiers, it was discovered that DNN and support vector machine (SVM) classifiers produce the best results. As a result, recent contributions have focused on developing ensemble approaches that outperform various EEG channel selection algorithms combined with DNNs and SVM [ 21 , 22 , 24 , 25 ]. These methods employ a combination of spatial filters, correlation-based, sequential-based, and binary harmony search-based EEG channel selection, and various classifiers (e.g., CNN [ 24 ], MLP-NN [ 22 ], SVM [ 19 ], linear discriminant analysis (LDA) [ 26 ] on one or more BCI competition datasets.…”

Section: Introductionmentioning

confidence: 99%

EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives

2022

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Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain’s motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10–30% of total channels, provided excellent performance compared to other existing studies.

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“…As a standard paradigm in brain-computer interfaces [7], MI has rapidly developed in recent years. Underlying this rapid development is the ability of MI to trigger contralateral explicit event-related desynchronization (ERD) and, in some cases, simultaneous ipsilateral event-related synchronization (ERS) by unilateral imaging movements [8] For instance, when picturing unilateral hand movements, the energy of mu rhythms (8)(9)(10)(11)(12) and beta rhythms (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) in the contralateral brain region is decreased (ERD), whereas the energy of mu rhythms and beta rhythms in the ipsilateral motor-sensory areas is increased (ERS) [9]. The spontaneity and classifiability of MI make it a critical factor in ensuring the availability and smoothness (the efficiency of information transmission) of the machine subsystem in BCI systems.…”

Section: Introductionmentioning

confidence: 99%

“…The spontaneity and classifiability of MI make it a critical factor in ensuring the availability and smoothness (the efficiency of information transmission) of the machine subsystem in BCI systems. Much of the current research on motion imagery has focused on the separability of MI, enhancing accuracy by examining feature extraction [10], channel selection [11], and classification methods [12].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Visual Guidance of Motor Imagery in Sustainable Brain–Computer Interfaces

et al. 2022

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Motor imagery brain–computer interface (MI-BCI) systems hold the possibility of restoring motor function and also offer the possibility of sustainable autonomous living for individuals with various motor and sensory impairments. When utilizing the MI-BCI, the user’s performance impacts the system’s overall accuracy, and concentrating on the user’s mental load enables a better evaluation of the system’s overall performance. The impacts of various levels of abstraction on visual guidance of mental training in motor imagery (MI) may be comprehended. We proposed hypotheses about the effects of visually guided abstraction on brain activity, mental load, and MI-BCI performance, then used the event-related desynchronization (ERD) value to measure the user’s brain activity, extracted the brain power spectral density (PSD) to measure the brain load, and finally classified the left- and right-handed MI through a support vector machine (SVM) classifier. The results showed that visual guidance with a low level of abstraction could help users to achieve the highest brain activity and the lowest mental load, and the highest accuracy rate of MI classification was 97.14%. The findings imply that to improve brain–computer interaction and enable those less capable to regain their mobility, visual guidance with a low level of abstraction should be employed when training brain–computer interface users. We anticipate that the results of this study will have considerable implications for human-computer interaction research in BCI.

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Neuro-evolutionary approach for optimal selection of EEG channels in motor imagery based BCI application

Cited by 29 publications

References 35 publications

Multimodal explainable AI predicts upcoming speech behavior in adults who stutter

Multimodal explainable AI predicts upcoming speech behavior in adults who stutter

EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives

Exploring the Visual Guidance of Motor Imagery in Sustainable Brain–Computer Interfaces

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