Characterization of Bimanual Cyclical Tasks From Single-Trial EEG-fNIRS Measurements

Jiang, Yichuan; Ma, Rui; Qi, Shichen; Ge, Sheng; Sun, Ziqi; Li, Yishu; Song, Jongbin; Zhang, Mingming

doi:10.1109/tnsre.2022.3144216

Cited by 14 publications

(10 citation statements)

References 55 publications

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“…RAIN-COMPUTER interface (BCI) can provide a communication pathway between the brain and external devices [1,2]. To date, a large number of studies have demonstrated that kinematic parameters of hand movements are strong relative to non-invasive electroencephalogram (EEG) signals [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Decoding Coordinated Directions of Bimanual Movements From EEG Signals

Zhang

Song

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Bimanual coordination is common in human daily life, whereas current research focused mainly on decoding unimanual movement from electroencephalogram (EEG) signals. Here we developed a brain-computer interface (BCI) paradigm of task-oriented bimanual movements to decode coordinated directions from movement-related cortical potentials (MRCPs) of EEG. Eight healthy subjects participated in the target-reaching task, including (1) performing leftward, midward, and rightward bimanual movements, and (2) performing leftward and rightward unimanual movements. A combined deep learning model of convolution neural network and bidirectional long short-term memory network was proposed to classify movement directions from EEG. Results showed that the average peak classification accuracy for three coordinated directions of bimanual movements reached 73.39 ± 6.35%. The binary classification accuracies achieved 80.24 ± 6.25, 82.62 ± 7.82, and 86.28 ± 5.50% for leftward versus midward, rightward versus midward and leftward versus rightward, respectively. We also compared the binary classification (leftward versus rightward) of bimanual, left-hand, and right-hand movements, and accuracies achieved 86.28 ± 5.50%, 75.67 ± 7.18%, and 77.79 ± 5.65%, respectively. The results indicated the feasibility of decoding human coordinated directions of task-oriented bimanual movements from EEG.

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Section: Introductionmentioning

confidence: 99%

Decoding Coordinated Directions of Bimanual Movements From EEG Signals

Zhang

Song

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Subsequent research has found that frontal α rhythm asymmetry indicates approach/withdrawal tendencies related to emotions rather than the valence of emotions per se [53]. To examine these dynamics in more depth, our study considered the entire induction period, calculating average amplitude changes in Delta (1-4 Hz), Theta (4-8 Hz), Alpha (8-12 Hz), Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and Gamma (30-80 Hz) rhythms for each channel under various emotional states. This approach is presented in Figure 15, which visually presents the differences between different emotions.…”

Section: Electroencephalography (Eeg) Data Analysismentioning

confidence: 99%

Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System

Chen,

Yu,

Wang

et al. 2024

Electronics

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Emotion recognition remains an intricate task at the crossroads of psychology and artificial intelligence, necessitating real-time, accurate discernment of implicit emotional states. Here, we introduce a pioneering wearable dual-modal device, synergizing functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to meet this demand. The first-of-its-kind fNIRS-EEG ensemble exploits a temporal convolutional network (TC-ResNet) that takes 24 fNIRS and 16 EEG channels as input for the extraction and recognition of emotional features. Our system has many advantages including its portability, battery efficiency, wireless capabilities, and scalable architecture. It offers a real-time visual interface for the observation of cerebral electrical and hemodynamic changes, tailored for a variety of real-world scenarios. Our approach is a comprehensive emotional detection strategy, with new designs in system architecture and deployment and improvement in signal processing and interpretation. We examine the interplay of emotions and physiological responses to elucidate the cognitive processes of emotion regulation. An extensive evaluation of 30 subjects under four emotion induction protocols demonstrates our bimodal system’s excellence in detecting emotions, with an impressive classification accuracy of 99.81% and its ability to reveal the interconnection between fNIRS and EEG signals. Compared with the latest unimodal identification methods, our bimodal approach shows significant accuracy gains of 0.24% for EEG and 8.37% for fNIRS. Moreover, our proposed TC-ResNet-driven temporal convolutional fusion technique outperforms conventional EEG-fNIRS fusion methods, improving the recognition accuracy from 0.7% to 32.98%. This research presents a groundbreaking advancement in affective computing that combines biological engineering and artificial intelligence. Our integrated solution facilitates nuanced and responsive affective intelligence in practical applications, with far-reaching impacts on personalized healthcare, education, and human–computer interaction paradigms.

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“…Third, compared to increasing movement types of unilateral limb, involving bilateral-limb movements to be discriminated from unilateral movements can improve multidimensional control and meanwhile maintain well decoding performance, which can be attributed to the distinct brain activation patterns of unilateral and bilateral movements [16]. In recent years, several studies have turned attention to the simultaneous bilateral movements' decoding from EEG signals, including bimanual center-out movements [17,18], bimanual reach-and-grasp movements [15], and bimanual cyclical tasks [19]. Actually, humans can perform tasks with two hands either simultaneously or sequentially.…”

Section: Neural Correlate and Movement Decoding Ofmentioning

confidence: 99%

Neural Correlate and Movement Decoding of Simultaneous-and-Sequential Bimanual Movements Using EEG Signals

Wang,

Bi,

Fei

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Bimanual coordination is important for developing a natural motor brain-computer interface (BCI) from electroencephalogram (EEG) signals, covering the aspects of bilateral arm training for rehabilitation, bimanual coordination for daily-life assistance, and also improving the multidimensional control of BCIs. For the same task targets of both hands, simultaneous and sequential bimanual movements are two different bimanual coordination manners. Planning and performing motor sequences are the fundamental abilities of humans, and it is more natural to execute sequential movements compared to simultaneous movements in many complex tasks. However, to date, for these two different manners in which two hands coordinated to reach the same task targets, the differences in the neural correlate and also the feasibility of movement discrimination have not been explored. In this study, we aimed to investigate these two issues based on a bimanual reaching task for the first time. Finally, neural correlates in the view of the movement-related cortical potentials, event-related oscillations, and source imaging showed unique neural encoding patterns of sequential movements. Besides, for the same task targets of both hands, the simultaneous and sequential bimanual movements were successfully discriminated in both pre-movement and movement execution periods. This study revealed the neural encoding patterns of sequential bimanual movements and presented its values in developing a more natural and good-performance motor BCI.

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Characterization of Bimanual Cyclical Tasks From Single-Trial EEG-fNIRS Measurements

Cited by 14 publications

References 55 publications

Decoding Coordinated Directions of Bimanual Movements From EEG Signals

Decoding Coordinated Directions of Bimanual Movements From EEG Signals

Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System

Neural Correlate and Movement Decoding of Simultaneous-and-Sequential Bimanual Movements Using EEG Signals

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