Brain-Computer Interface: Advancement and Challenges

Mridha, M. F.; Das, Sujoy Chandra; Kabir, Md. Mohsin; Lima, Aklima Akter; Islam, Rashedul; Watanobe, Yutaka

doi:10.3390/s21175746

Cited by 119 publications

(79 citation statements)

References 316 publications

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“…A vexing finding in brains is that despite internal consistency and intact function, recordings show that, to an outside observer, brain representations seem to drift and change over days and weeks (for e.g. see Marks and Goard (2021)), causing difficulties, for example, in designing a brain controller interface (BCI) (Mridha et al, 2021). By training a classifier to predict task identifier from latent z vector, we show that the internal-generated representations in our model reveal similar dynamics (Fig.…”

Section: Pretrained Network No Longer Requires Task Labels: Latent Up...mentioning

confidence: 99%

Thalamus: a brain-inspired algorithm for biologically-plausible continual learning and disentangled representations

Hummos¹

2022

Preprint

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Animals thrive in a constantly changing environment and leverage the temporal structure to learn well-factorized causal representations. In contrast, traditional neural networks suffer from forgetting in changing environments and many methods have been proposed to limit forgetting with different trade-offs. Inspired by the brain thalamocortical circuit, we introduce a simple algorithm that uses optimization at inference time to generate internal representations of temporal context and to infer current context dynamically, allowing the agent to parse the stream of temporal experience into discrete events and organize learning about them. We show that a network trained on a series of tasks using traditional weight updates can infer tasks dynamically using gradient descent steps in the latent task embedding space (latent updates). We then alternate between the weight updates and the latent updates to arrive at Thalamus, a task-agnostic algorithm capable of discovering disentangled representations in a stream of unlabeled tasks using simple gradient descent. On a continual learning benchmark, it achieves competitive end average accuracy and demonstrates knowledge transfer. After learning a subset of tasks it can generalize to unseen tasks as they become reachable within the well-factorized latent space, through one-shot latent updates. The algorithm meets many of the desiderata of an ideal continually learning agent in open-ended environments, and its simplicity suggests fundamental computations in circuits with abundant feedback control loops such as the thalamocortical circuits in the brain.Preprint. Under review.

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Section: Pretrained Network No Longer Requires Task Labels: Latent Up...mentioning

confidence: 99%

Thalamus: a brain-inspired algorithm for biologically-plausible continual learning and disentangled representations

Hummos¹

2022

Preprint

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“…A brain-computer interface (BCI) allows disabled people to communicate with the outside world and control external devices based on various neuroimaging technology such as electroencephalography (EEG), magnetoencephalography (MEG) or positron emission tomography (PET) [1][2][3]. Among them, EEG-based BCIs have received increasing attention due to its ease of use, low cost and high temporal resolution compared to MEG and PET [4,5]. For example, a BCI based on EEG signals can be used to help paralyzed patients control wheelchair without the involvement of neural muscles.…”

Section: Introductionmentioning

confidence: 99%

Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition

Xiong

Wei

2022

PLoS ONE

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One of the major reasons that limit the practical applications of a brain-computer interface (BCI) is its long calibration time. In this paper, we propose a novel approach to reducing the calibration time of motor imagery (MI)-based BCIs without sacrificing classification accuracy. The approach aims to augment the training set size of a new subject by generating artificial electroencephalogram (EEG) data from a few training trials initially available. The artificial EEG data are obtained by first performing empirical mode decomposition (EMD) and then mixing resulting intrinsic mode functions (IMFs). The original training trials are aligned to common reference point with Euclidean alignment (EA) method prior to EMD and pooled together with artificial trials as the expended training set, which is input into a linear discriminant analysis (LDA) classifier or a logistic regression (LR) classifier. The performance of the proposed algorithm is evaluated on two motor imagery (MI) data sets and compared with that of the algorithm trained with only real EEG data (Baseline) and the algorithm trained with expanded EEG data by EMD without data alignment. The experimental results showed that the proposed algorithm can significantly reduce the amount of training data needed to achieve a given performance level and thus is expected to facilitate the real-world applications of MI-based BCIs.

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“…However, there are still open research problems, such as the real-time processing of EEG signal classification and the optimization of ML algorithms for implementation on embedded systems or edge computing devices. Hence, research on and development of reliable, efficient, and robust systems for EEG signal classification, among others, should be pursued [16,68]. The complexity of human movements for the manipulation of tools is very high and diverse; for an adult human brain that has automated different movements, it does not represent a major effort, however, for ML it requires the management of precise information inputs that allow programming and execution of free movement.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

et al. 2022

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In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices.

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Brain-Computer Interface: Advancement and Challenges

Cited by 119 publications

References 316 publications

Thalamus: a brain-inspired algorithm for biologically-plausible continual learning and disentangled representations

Thalamus: a brain-inspired algorithm for biologically-plausible continual learning and disentangled representations

Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

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