Neural Decoding of Chinese Sign Language With Machine Learning for Brain–Computer Interfaces

Wang, Pengpai; Zhou, Yanqing; Li, Zhongnian; Huang, Shuo; Zhang, Daoqiang

doi:10.1109/tnsre.2021.3137340

Cited by 7 publications

(2 citation statements)

References 73 publications

(69 reference statements)

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“…Twenty subjects (25×14-year-old) were recruited according to the experimental setup, including 9 females and 11 males [42]. All subjects were required to be in good health and full of energy without brain surgery or brain-related diseases.…”

Section: Data Collection and Pre-processingmentioning

confidence: 99%

MTRT: Motion Trajectory Reconstruction Transformer for EEG-Based BCI Decoding

Wang

Gong

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Brain computer interface (BCI) is a system that directly uses brain neural activities to communicate with the outside world. Recently, the decoding of the human upper limb based on electroencephalogram (EEG) signals has become an important research branch of BCI. Even though existing research models are capable of decoding upper limb trajectories, the performance needs to be improved to make them more practical for real-world applications. This study is attempt to reconstruct the continuous and nonlinear multi-directional upper limb trajectory based on Chinese sign language. Here, to reconstruct the upper limb motion trajectory effectively, we propose a novel Motion Trajectory Reconstruction Transformer (MTRT) neural network that utilizes the geometric information of human joint points and EEG neural activity signals to decode the upper limb trajectory. Specifically, we use human upper limb bone geometry properties as reconstruction constraints to obtain more accurate trajectory information of the human upper limbs. Furthermore, we propose a MTRT neural network based on this constraint, which uses the shoulder, elbow, and wrist joint point information and EEG signals of brain neural activity during upper limb movement to train its parameters. To validate the model, we collected the synchronization information of EEG signals and upper limb motion joint points of 20 subjects. The experimental results show that the reconstruction model can accurately reconstruct the motion trajectory of the shoulder, elbow, and wrist of the upper limb, achieving superior performance than the compared methods. This research is very meaningful to decode the limb motion parameters for BCI, and it is inspiring for the motion decoding of other limbs and other joints.

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Section: Data Collection and Pre-processingmentioning

confidence: 99%

MTRT: Motion Trajectory Reconstruction Transformer for EEG-Based BCI Decoding

Wang

Gong

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…We extend this approach by incorporating both RF and DT classifiers within a unified framework and thereby enhancing the robustness of our classification model. Neural networks were used to diagnose ADHD from EEG data in [22]. In contrast, our method offers interpretable insights by considering specific brain regions, thus making it more transparent and clinically applicable.…”

Section: Introductionmentioning

confidence: 99%

Regional Contribution in Electrophysiological-Based Classifications of Attention Deficit Hyperactive Disorder (ADHD) Using Machine Learning

Chauhan,

Choi

2023

Computation

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Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition in children and is characterized by challenges in maintaining attention, hyperactivity, and impulsive behaviors. Despite ongoing research, we still do not fully understand what causes ADHD. Electroencephalography (EEG) has emerged as a valuable tool for investigating ADHD-related neural patterns due to its high temporal resolution and non-invasiveness. This study aims to contribute to diagnostic accuracy by leveraging EEG data to classify children with ADHD and healthy controls. We used a dataset containing EEG recordings from 60 children with ADHD and 60 healthy controls. The EEG data were captured during cognitive tasks and comprised signals from 19 channels across the scalp. Our primary objective was to develop a machine learning model capable of distinguishing ADHD subjects from controls using EEG data as discriminatory features. We employed several well-known classifiers, including a support vector machine, random forest, decision tree, AdaBoost, Naive Bayes, and linear discriminant analysis, to discern distinctive EEG patterns. To further enhance classification accuracy, we explored the impact of regional data on the classification outcomes. We arranged the EEG data according to the brain regions from which they were derived (namely frontal, temporal, central, parietal, and occipital) and examined their collective effects on the accuracy of our classifications. Notably, we considered combinations of three regions at a time and found that certain combinations led to enhanced accuracy. Our findings underscore the potential of EEG-based classification in distinguishing children with ADHD from healthy controls. The Naive Bayes classifier yielded the highest accuracy (84%) when applied to specific region combinations. Moreover, we evaluated the classification performance based on hemisphere-specific EEG data and found promising results, particularly when using the right hemisphere region channels.

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