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Lin, Chin-Teng

doi:10.5405/jmbe.30.4.07

Cited by 37 publications

(10 citation statements)

References 28 publications

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“…As one of the most anticipated developmental directions of human–computer interactions in the future, BCI systems can conduct human–computer interaction through the implicit information contained in EEGs [4]. There have already been breakthroughs in several applications, such as robotic arm control [6,7,8], drowsiness driving detection [9] and emotion recognition [10,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Flexible Multi-Layer Semi-Dry Electrode for Scalp EEG Measurements at Hairy Sites

Hua

Tang

et al. 2019

Micromachines

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One of the major challenges of daily wearable electroencephalogram (EEG) monitoring is that there are rarely suitable EEG electrodes for hairy sites. Wet electrodes require conductive gels, which will dry over the acquisition time, making them unstable for long-term EEG monitoring. Additionally, the electrode–scalp impedances of most dry electrodes are not adequate for high quality EEG collection at hairy sites. In view of the above problems, a flexible multi-layer semi-dry electrode was proposed for EEG monitoring in this study. The semi-dry electrode contains a flexible electrode body layer, foam layer and reservoir layer. The probe structure of the electrode body layer enables the electrode to work effectively at hairy sites. During long-term EEG monitoring, electrolytes stored in the reservoir layer are continuously released through the foam layer to the electrode–scalp interface, ensuring a lower electrode–scalp contact impedance. The experimental results showed that the average electrode–scalp impedance of the semi-dry electrode at a hairy site was only 23.89 ± 7.44 KΩ at 10 Hz, and it was lower than 40 KΩ over a long-term use of 5 h. The electrode performed well in both static and dynamic EEG monitoring, where the temporal correlation with wet electrode signals at the hairy site could reach 94.25% and 90.65%, respectively, and specific evoked EEG signals could be collected. The flexible multi-layer semi-dry electrode can be well applied to scalp EEG monitoring at hairy sites, providing a promising solution for daily long-term monitoring of wearable EEGs.

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Section: Introductionmentioning

confidence: 99%

Flexible Multi-Layer Semi-Dry Electrode for Scalp EEG Measurements at Hairy Sites

Hua

Tang

et al. 2019

Micromachines

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“…Typically, EEG power spectra are quantified and divided by frequency ranges, such as delta (1–3 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta (13–31 Hz), and gamma (32–50 Hz) bands ( Tatum, 2014 ), with these oscillations within different frequency bands representative of various brain activations and conditions. Several publications in recent years have appeared documenting that EEG is a robust approach to evaluate physiological states, including alertness, drowsiness ( Lin et al, 2010 ; Bajaj et al, 2020 ), fatigue ( Monteiro et al, 2019 ; Xu et al, 2019 ), and stress ( Xia et al, 2018 ; Asif et al, 2019 ). To assess the changes of physiological states exploiting EEG signals, five parameters are employed to develop the proposed pentagonal physiological state indicator: attention, stress, fatigue, and left and right brain activity levels.…”

Section: Introductionmentioning

confidence: 99%

Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings

Chang

Tsai

et al. 2021

Front. Hum. Neurosci.

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Mental state changes induced by stimuli under experimental settings or by daily events in real life affect task performance and are entwined with physical and mental health. In this study, we developed a physiological state indicator with five parameters that reflect the subject’s real-time physiological states based on online EEG signal processing. These five parameters are attention, fatigue, stress, and the brain activity shifts of the left and right hemispheres. We designed a target detection experiment modified by a cognitive attention network test for validating the effectiveness of the proposed indicator, as such conditions would better approximate a real chaotic environment. Results demonstrated that attention levels while performing the target detection task were significantly higher than during rest periods, but also exhibited a decay over time. In contrast, the fatigue level increased gradually and plateaued by the third rest period. Similar to attention levels, the stress level decreased as the experiment proceeded. These parameters are therefore shown to be highly correlated to different stages of the experiment, suggesting their usage as primary factors in passive brain-computer interfaces (BCI). In addition, the left and right brain activity indexes reveal the EEG neural modulations of the corresponding hemispheres, which set a feasible reference of activation for an active BCI control system, such as one executing motor imagery tasks. The proposed indicator is applicable to potential passive and active BCI applications for monitoring the subject’s physiological state change in real-time, along with providing a means of evaluating the associated signal quality to enhance the BCI performance.

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“…A mental speller is one of the most popular BCI applications, which allows LIS patients to communicate a message [7,[25][26][27][28]. In addition to a mental speller, different BCI applications have been developed, such as a robotic arm [29][30][31], mobile robot [32], wheelchair [33][34][35][36][37][38], mouse [39,40], smart home system [41], mobile phone application [42], and various games [43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review

2020

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A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance.

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Untitled

Cited by 37 publications

References 28 publications

Flexible Multi-Layer Semi-Dry Electrode for Scalp EEG Measurements at Hairy Sites

Flexible Multi-Layer Semi-Dry Electrode for Scalp EEG Measurements at Hairy Sites

Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings

Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review

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