A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery

Zhang, Ce; Eskandarian, Azim

doi:10.1109/embc44109.2020.9176705

Cited by 14 publications

(7 citation statements)

References 17 publications

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“…Also, manual signal processing and feature extraction are usually hard to extract implicit features and hard to filter the signal characteristics generated by different subjects. Therefore, many researchers try to find solutions to improve the conventional machine learning methods, like improving the current signal prepossessing method [27], modifying current classic classifier or proposing new EEG-based machine learning systems to improve the performance of estimation. Ruo-Nan Duan [28] proposes a differential entropy feature for EEG and shows its advantage compared with traditional energy spectrum feature.…”

Section: ) Conventional Machine Learning Approachmentioning

confidence: 99%

EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals

Ding¹,

Zhang²,

Eskandarian³

2022

Preprint

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A lack of driver's vigilance is the main cause of most vehicle crashes. Electroencephalography(EEG) has been reliable and efficient tool for drivers' drowsiness estimation. Even though previous studies have developed accurate and robust driver's vigilance detection algorithms, these methods are still facing challenges on following areas: (a) small sample size training, (b) anomaly signal detection, and (c) subject-independent classification. In this paper, we propose a generalized fewshot model, namely EEG-Fest, to improve aforementioned drawbacks. The EEG-Fest model can (a) classify the query sample's drowsiness with a few samples, (b) identify whether a query sample is anomaly signals or not, and (c) achieve subject independent classification. The proposed algorithm achieves state-of-the-art results on the SEED-VIG dataset and the SADT dataset. The accuracy of the drowsy class achieves 92% and 94% for 1-shot and 5-shot support samples in the SEED-VIG dataset, and 62% and 78% for 1-shot and 5-shot support samples in the SADT dataset.

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Section: ) Conventional Machine Learning Approachmentioning

confidence: 99%

EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals

Ding¹,

Zhang²,

Eskandarian³

2022

Preprint

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“…The classifier employed was a Linear Discriminant Analysis (LDA) [34]. It has been commonly used on previous investigations [16,35,36] due to its high reported precision and short computational time [37,38]. Consequently, all fullstatic trials for each subject and experimental session were considered for cross-validation.…”

Section: Classificationmentioning

confidence: 99%

Brain Symmetry Analysis during the Use of a BCI Based on Motor Imagery for the Control of a Lower-Limb Exoskeleton

et al. 2021

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Brain–Computer Interfaces (BCI) are systems that allow external devices to be controlled by means of brain activity. There are different such technologies, and electroencephalography (EEG) is an example. One of the most common EEG control methods is based on detecting changes in sensorimotor rhythms (SMRs) during motor imagery (MI). The aim of this study was to assess the laterality of cortical function when performing MI of the lower limb. Brain signals from five subjects were analyzed in two conditions, during exoskeleton-assisted gait and while static. Three different EEG electrode configurations were evaluated: covering both hemispheres, covering the non-dominant hemisphere and covering the dominant hemisphere. In addition, the evolution of performance and laterality with practice was assessed. Although sightly superior results were achieved with information from all electrodes, differences between electrode configurations were not statistically significant. Regarding the evolution during the experimental sessions, the performance of the BCI generally evolved positively the higher the experience was.

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“…However, most of the existing EEG-based BCIs are datadriven because specific physical properties of intention are still a mystery. Restricted by data-driven basic machine learning (ML) algorithms(e.g., Support Vector Machines (SVM) [8], Neural Network (NN), and Bayesian Classifier [9]), traditional EEG-based BCIs usually model the biological EEG-intention system as a black box [8], [10].…”

Section: Introductionmentioning

confidence: 99%

EEGG: An Analytic Brain-Computer Interface Algorithm

Liu

Wang

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Objective. Modeling the brain as a white box is vital for investigating the brain. However, the physical properties of the human brain are unclear. Therefore, BCI algorithms using EEG signals are generally a data-driven approach and generate a black-or gray-box model. This paper presents the first EEG-based BCI algorithm (EEG-BCI using Gang neurons, EEGG) decomposing the brain into some simple components with physical meaning and integrating recognition and analysis of brain activity. Approach. Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relation frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neurons. A total of 4,906 EEG data of left-and right-hand motor imagery(MI) from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG's classification performance by cross-subject accuracy. Secondly, this paper transformed the trained EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified using the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCIbased analysis of the brain. Main results. (1) EEGG was more robust than typical "CSP+" algorithms for the lowquality data. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that interactive components between brain regions suppressed ERD/ERS effects on classification. This means that generating fine hand intention needs more centralized activation in the brain. Significance. EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting the Taylor series (in analogy with the data-driven but human-readable Fourier transform and frequency spectrum), which offers a novel frame for analysis of the brain.

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A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery

Cited by 14 publications

References 17 publications

EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals

EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals

Brain Symmetry Analysis during the Use of a BCI Based on Motor Imagery for the Control of a Lower-Limb Exoskeleton

EEGG: An Analytic Brain-Computer Interface Algorithm

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