Attention Optimization Method for EEG via the TGAM

Yu, Wu; Xie, Ning

doi:10.1155/2020/6427305

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…As showcased in Fig. 6d, we apply FFT to the original EEG signals, delineating frequency bands and deriving eSense values via NeuroSky's algorithm 72,73 . This algorithm can express the mental state information (attention and meditation) of the human brain with eSense values.…”

Section: Clinical Detection and Depression Detectionmentioning

confidence: 99%

A 3.55-μm ultrathin, skin-like mechanoresponsive, compliant, and seamless ionic conductive electrode for epidermal electrophysiological signal acquisition and human-machine-interaction

Zhang,

Chen,

Ying

et al. 2024

Preprint

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Flexible ionic conductive electrodes, as a fundamental component for electrical signal transmission, play a crucial role in skin-surface electronic devices. Developing a skin-seamlessly electrode that can effectively capture long-term, artifacts-free, and high-quality electrophysiological signals, remains a challenge. Herein, we report an ultra-thin and dry electrode consisting of deep eutectic solvent (DES) and zwitterions (CEAB), which exhibit significantly lower reactance and noise in both static and dynamic monitoring compared to standard Ag/AgCl gel electrodes. Our electrodes have skin-like mechanical properties (strain-rigidity relationship and flexibility), outstanding adhesion, and high electrical conductivity. Consequently, they excel in consistently capturing high-quality epidermal biopotential signals, such as the electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. Furthermore, we demonstrate the promising potential of the electrodes in clinical applications by effectively distinguishing aberrant EEG signals associated with depressive patients. Meanwhile, through the integration of CEAB electrodes with digital processing and advanced algorithms, valid gesture control of artificial limbs based on EMG signals is achieved, highlighting its capacity to significantly enhance human-machine interaction.

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Section: Clinical Detection and Depression Detectionmentioning

confidence: 99%

A 3.55-μm ultrathin, skin-like mechanoresponsive, compliant, and seamless ionic conductive electrode for epidermal electrophysiological signal acquisition and human-machine-interaction

Zhang,

Chen,

Ying

et al. 2024

Preprint

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“…We added data collected by a portable EEG device TGAM to calculate attention, which reflected how focus the participants were. Particularly, an optimization algorithm was used to improve EEG data [20,51], and we generate two parameters: one is attention whose value varying in the range of 0-100, and the other is attention level divided into eight grades (0-7).…”

Section: Embodiment Of Individual: Attentionmentioning

confidence: 99%

The Time Perception Control and Regulation in VR Environment

Liu¹,

Shi²,

He³

et al. 2021

Preprint

Self Cite

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To adapt to different environments, human circadian rhythms will be constantly adjusted as the environment changes, which follows the principle of survival of the fittest. According to this principle, objective factors (such as circadian rhythms, and light intensity) can be utilized to control time perception. The subjective judgment on the estimation of elapsed time is called time perception. In the physical world, factors that can affect time perception, represented by illumination, are called the Zeitgebers. In recent years, with the development of Virtual Reality (VR) technology, effective control of zeitgebers has become possible, which is difficult to achieve in the physical world.

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“…TGAM module has the characteristics of low power consumption, small size, comfortable wearing, accurate signal, automatic resolution, and has been applied in many fields [15][16]. The degree of concentration and relaxation that can be automatically resolved can be used for the design of an intelligent wheelchair and is not affected by driving observation, limb fatigue, environmental noise, road conditions avoidance, and other factors.…”

Section: Introductionmentioning

confidence: 99%

A smart brain controlled wheelchair based on TGAM

Yu,

Xie

2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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A brain-controlled wheelchair system based on TGAM module is proposed, which can improve people quality of life which suffering from severe movement disorders. The TGAM is used as the EEG signals acquisition and processing module. The EEG data is transmitted to the micro-controller through the Bluetooth module. The data is validated and the concentration parameter is parsed, the concentration value is converted into the speed parameter of the wheelchair, and the key state is converted into the wheelchair movement direction parameter, to control the wheelchair movement according to the user's real-time concentration. The test results show that the TGAM module can accurately collect EEG signals, and the micro-controller can analyze the concentration data, and control the wheelchair's forward, backward and turn through the motor. The intelligent wheelchair is simple, easy to operate, and stable in function. It can be operated only through the user's concentration, providing a new convenient wheelchair control mode for people with walking difficulties.

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Attention Optimization Method for EEG via the TGAM

Cited by 7 publications

References 31 publications

A 3.55-μm ultrathin, skin-like mechanoresponsive, compliant, and seamless ionic conductive electrode for epidermal electrophysiological signal acquisition and human-machine-interaction

A 3.55-μm ultrathin, skin-like mechanoresponsive, compliant, and seamless ionic conductive electrode for epidermal electrophysiological signal acquisition and human-machine-interaction

The Time Perception Control and Regulation in VR Environment

A smart brain controlled wheelchair based on TGAM

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