Effects of Soft Drinks on Resting State EEG and Brain–Computer Interface Performance

Meng, Jianjun; Mundahl, John; Streitz, Taylor D.; Maile, Kaitlin; Gulachek, Nicholas; He, Jeffrey; He, Bin

doi:10.1109/access.2017.2751069

Cited by 30 publications

(17 citation statements)

References 39 publications

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“…It extracts EEG signals by using timefrequency analysis methods. Jianjun Meng et al [14] used PSD to reveal the effect of caffeine and sugar intake on BCI online performance and resting brain signals. The common time-frequency analysis is short-time Fourier transform (STFT), Hilbert Huang transform (HHT), and wavelet transform (WT) [15], etc.…”

Section: Related Workmentioning

confidence: 99%

MLP With Riemannian Covariance for Motor Imagery Based EEG Analysis

et al. 2020

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Stroke is one of the leading causes of disability and incidence. For the treatment of prognosis of stroke patients, Motor imagery (MI) as a novel experimental paradigm, clinically it is effective because MI based Brain-Computer interface system can promote rehabilitation of stroke patients. There is being a hot and challenging topic to recognize multi-class motor imagery action classification accurately based on electroencephalograph (EEG) signals. In this work, we propose a novel framework named MRC-MLP. Multiple Riemannian covariance is used for EEG feature extraction. We make a multi-scale spectral division to filter EEG signals. They consist of different frequency bandwidths name sub-band. We concatenate and vectorize features extracted by Riemannian covariance on each sub-band. We design a fully connected MLP model with an improved loss function for motor imagery EEG classification. Furthermore, our proposed method MRC-MLP outperforms state-of-the-art methods and achieves approximately mean accuracy with 76%.

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Section: Related Workmentioning

confidence: 99%

MLP With Riemannian Covariance for Motor Imagery Based EEG Analysis

et al. 2020

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“…Although caffeine has been studied for more than a 100 years, more research is necessary to better understand how brain activity is affected by caffeine consumption (Meng et al, 2017 ; van Son et al, 2018 ; Franco-Alvarenga et al, 2019 ; Tarafdar et al, 2019 ; Ueda and Nakao, 2019 ). Electrophysiological technology with event-related-potential (ERP) component detection, such as P50, N200, and P300, has been used for the measurement of brain activity.…”

Section: Introductionmentioning

confidence: 99%

Effects of Caffeine on Event-Related Potentials and Neuropsychological Indices After Sleep Deprivation

Chen

Zhang

Yang

et al. 2020

Front. Behav. Neurosci.

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Objective: Caffeine is a central nervous system stimulant that can effectively alleviate brain fatigue and low cognitive efficiency induced by total sleep deprivation (TSD). Recent studies have demonstrated that caffeine can improve subjective attention and objective behavioral metrics, such as arousal level, reaction time, and memory efficiency. However, only a few studies have examined the electrophysiological changes caused by the caffeine in humans following sleep disturbance. In this study, an event-related potential (ERP) technique was employed to measure the behavioral, cognitive, and electrophysiological changes produced by caffeine administration after TSD. Methods: Sixteen healthy subjects within-subject design performed a visual Go/No-Go task with simultaneous electroencephalogram recording. Behavioral and ERP data were evaluated after 36 h of TSD, and the effects of ingestion of either 400 mg of caffeine or placebo were compared in a double-blind randomized design. Results: Compared with placebo administration, the Go hit rates were significantly enhanced in the caffeine condition. A simple effect analysis revealed that, compared with baseline, the Go-P2 amplitude was significantly enhanced after TSD in the caffeine consumption condition. A significant main effect of the drug was found on No-Go-P2, No-Go-N2 amplitude, and Go-P2 latency before and after TSD. Conclusion: Our findings indicate that caffeine administration has acute effects on improving the efficiency of individual automatic reactions and early cognitive processes. Caffeine was related to the preservation of an individual's arousal level and accelerated response-related decisions, while subjects' higher-level recognition had limited improvement with prolonged awareness.

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“…It is worthy to notice that the same frequency band cannot be defined for two or more users due to the intrinsic variability between subjects [10], [16]. Furthermore, the SMRs rhythms is highly dependent to subject health status of subjects or the environment [17]. A feasible architecture is, therefore, required for the automatic selection of active spectra susceptible to contain useful information for each subject.…”

Section: Figure 1: Typical Eeg Signal Processing Chainmentioning

confidence: 99%

An Effective Zeros-Time Windowing Strategy to Detect Sensorimotor Rhythms Related to Motor Imagery EEG Signals

2020

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Brain-computer interface (BCI) acquires, analyzes and transforms human brain activity to control commands allowing as such disabled people to communicate or control external devices. A motor imagery-based BCI enables patients to control artificial peripherals and communicate with the outside world by merely thinking of the task such as, e.g., the imagination of left-hand, right-hand, or foot movement. The mere intention of moving one of the limbs triggers neural activity, which is induced in the primary sensorimotor areas like that observed with real executed movements. Tracking generated sensorimotor rhythms (SMRs) and extracting robust and informative features from electroencephalogram (EEG) signals are challenging due to the time-varying nature of EEG signals and the inter-human variability. In this paper, we proposed an EEG-zeros-time windowing (E2ZTW) approach based on a highly decaying window function to track SMRs and identify the temporal epochs containing useful information without any prior information on the trigger. The proposed approach involves the application of the group-delay function, allowing the improvement of the spectral resolution due to the additive property of the function on individual resonances. Some algorithms were integrated into the proposed approach, such as the common spatial pattern algorithm, which is used to extract features and linear discriminant analysis and a convolutional neural network, which are used for the classification of the features. The effectiveness of the proposed approach in tracking the SMRs rhythms is evaluated in terms of accuracy. Experiments were performed on three public datasets provided by BCI competition for 17 subjects. Following experimental results, it is shown that discrimination between the left-and right-hand movements can be achieved within a few seconds with high classification accuracy. As compared to other state-of-art techniques, the proposed approach achieves an average classification accuracy and standard error values of 82% and 13, respectively, thereby outperforming existing algorithm by an accuracy mean of 2%.

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Effects of Soft Drinks on Resting State EEG and Brain–Computer Interface Performance

Cited by 30 publications

References 39 publications

MLP With Riemannian Covariance for Motor Imagery Based EEG Analysis

MLP With Riemannian Covariance for Motor Imagery Based EEG Analysis

Effects of Caffeine on Event-Related Potentials and Neuropsychological Indices After Sleep Deprivation

An Effective Zeros-Time Windowing Strategy to Detect Sensorimotor Rhythms Related to Motor Imagery EEG Signals

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