Browsing by Author "Yeh, Chia-Lung"

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Development of a Smart Helmet for Strategical BCI Applications
(2019-04-02)
Ko, Li-Wei
;
Chang, Yang
;
Wu, Pei-Lun
;
Tzou, Heng-An
;
Chen, Sheng-Fu
Conducting electrophysiological measurements from human brain function provides a medium for sending commands and messages to the external world, as known as a brain-computer interface (BCI). In this study, we proposed a smart helmet which integrated the novel hygroscopic sponge electrodes and a combat helmet for BCI applications; with the smart helmet, soldiers can carry out extra tasks according to their intentions, i.e., through BCI techniques. There are several existing BCI methods which are distinct from each other; however, mutual issues exist regarding comfort and user acceptability when utilizing such BCI techniques in practical applications; one of the main challenges is the trade-off between using wet and dry electroencephalographic (EEG) electrodes. Recently, several dry EEG electrodes without the necessity of conductive gel have been developed for EEG data collection. Although the gel was claimed to be unnecessary, high contact impedance and low signal-to-noise ratio of dry EEG electrodes have turned out to be the main limitations. In this study, a smart helmet with novel hygroscopic sponge electrodes is developed and investigated for long-term usage of EEG data collection. The existing electrodes and EEG equipment regarding BCI applications were adopted to examine the proposed electrode. In the impedance test of a variety of electrodes, the sponge electrode showed performance averaging 118 k, which was comparable with the best one among existing dry electrodes, which averaged 123 k. The signals acquired from the sponge electrodes and the classic wet electrodes were analyzed with correlation analysis to study the effectiveness. The results indicated that the signals were similar to each other with an average correlation of 90.03% and 82.56% in two-second and ten-second temporal resolutions, respectively, and 97.18% in frequency responses. Furthermore, by applying the proposed differentiable power algorithm to the system, the average accuracy of 21 subjects can reach 91.11% in the steady-state visually evoked potential (SSVEP)-based BCI application regarding a simulated military mission. To sum up, the smart helmet is capable of assisting the soldiers to execute instructions with SSVEP-based BCI when their hands are not available and is a reliable piece of equipment for strategical applications.
Novel Moisture Retention Sponge Electrodes for Developing a Wireless EEG SSVEP-based BCI System
(2018-01-01)
Ko, Li-Wei
;
Chang, Yang
;
Wu, Pei-Lun
;
Lu, Yi-Chen
;
Yeh, Chia-Lung
Brain computer interface (BCI) is a method of communication between human brain and external device. Among the existing BCIs, steady-state visual evoked potential (SSVEP) is a common BCI technique due to its excellent signal-to-noise ratio (SNR) and relative immunity against artifacts. However, it remains some limitations regarding applying such technique in practical scenarios; one of the limitations is the trade-off between wet and dry EEG electrodes. In this study, we proposed a novel moisture retention sponge electrode and an innovative differentiable power algorithm for developing SSVEP-based BCI applications. The test of impedance suggested that the sponge electrode exhibited lower impedance compared with metal electrodes, which is adequate when hair is present. The proposed algorithm also demonstrated better accuracy regarding SSVEP-based BCI application. Adopting both innovations could strengthen the feasibility of the real-world applications of BCI.