Wearable EEG Device Research Articles

Behavior State Analysis through Brain Computer Interface using Wearable EEG Devices: A Review

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG) based human computer interface in order to enhance the quality of life for medically as well as non-medical applications. Industry and community of research have been attracted by wireless EEG reading devices and they are easily available in the market. Such technology can be incorporated into psychology, anesthesiology, and for real-time patients monitoring. A brain computer interface (BCI) is a direct communication channel between the human brain and the digital computer. In this paper, we present a review on characteristics and specification of EEG-based human computer interfaces for real-time applications using wearable or wireless EEG devices.

Behaviour state analysis through brain computer interface using wearable EEG devices: a review

Behaviour state analysis through brain computer interface using wearable EEG devices: a review

Autonomous OA Removal in Real-Time from Single Channel EEG Data on a Wearable Device Using a Hybrid Algebraic-Wavelet Algorithm

Electroencephalography (EEG) is a non-invasive technique to record brain activities in natural settings. Ocular Artifacts (OA) usually contaminates EEG signals, removal of which is critical for accurate feature extraction and classification. With the increasing adoption of wearable technologies, single-channel real-time EEG systems that often require real-time signal processing for immediate real-time feedback are becoming more prevalent. However, traditional OA removal algorithms usually require multiple channels of EEG data, are computationally expensive, and do not perform well in real-time. In this article, a new hybrid algorithm is proposed that autonomously detects OA and subsequently removes OA from a single-channel steaming EEG data in real-time. The proposed single EEG channel algorithm also does not require additional reference electrooculography (EOG) channel. The algorithm has also been implemented on an embedded hardware platform of single channel wearable EEG system (NeuroMonitor). The algorithm first detects the OA zones using an Algebraic approach and then removes these artifacts from the detected OA zones using the Discrete Wavelet Transform (DWT) decomposition method. The de-noising technique is applied only to the OA zone, which minimizes loss of neural information outside the OA zone. A qualitative and quantitative performance evaluation was carried out with a 0.5s epoch in overlapping sliding window technique using time-frequency analysis, mean square coherence, and correlation coefficient statistics. The hybrid OA removal algorithm demonstrated real-time operation with 3s latency on the PSoC-3-microcontroller-based EEG system. Successful implementation of OA removal from single-channel real-time EEG data using the proposed algorithm shows promise for real-time feedback applications of wearable EEG devices.

High performance wearable two-channel hybrid BCI system with eye closure assist.

Generally, eye closure (EC) and eye opening (EO)-based alpha blocking has widely recognized advantages, such as being easy to use, requiring little user training, while motor imagery (MI) is difficult for some users to have concrete feelings. This study presents a hybrid brain-computer interface (BCI) combining MI and EC strategies - such an approach aims to overcome some disadvantages of MI-based BCI, improve the performance and universality of the BCI. The EC/EO is employed to control the machine to switch in different states including forward, stop, changing direction motions, while the MI is used to control the machine to turn left or right for 90° by imagining the hands grasp motions when the system is switched into "changing direction" state. Additionally, a wearable two-channel EEG device is utilized in order to increase the efficiency of EEG processing and improving the practical utility. Results show that proposed hybrid system can generate four control commands with the average accuracy of 87.72%, which is higher than only using MI. Besides, it is possible to reach the same good accuracy using two-channel EEG as with usual multi-channel EEG.

An Inflatable and Wearable Wireless System for Making 32-Channel Electroencephalogram Measurements.

Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain-computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications.

ウェアラブル脳波計によるポータブルな脳波実験系の構築

本稿では，我々が取り組んでいるポータブルな脳波実験系の構築について解説する．そのために，まず，可搬であり簡易に計測が可能なウェアラブル脳波計について紹介する．続いて，ウェアラブル脳波計を用いたポータブルな脳波実験系の例として，ワークロードを推定するための実験系について解説し，歩行中であってもワークロードに関連する脳活動を計測することが可能であることを示す．

Detection of eye blink artifacts from single prefrontal channel electroencephalogram

Eye blinks are one of the most influential artifact sources in electroencephalogram (EEG) recorded from frontal channels, and thereby detecting and rejecting eye blink artifacts is regarded as an essential procedure for improving the quality of EEG data. In this paper, a novel method to detect eye blink artifacts from a single-channel frontal EEG signal was proposed by combining digital filters with a rule-based decision system, and its performance was validated using an EEG dataset recorded from 24 healthy participants. The proposed method has two main advantages over the conventional methods. First, it uses single-channel EEG data without the need for electrooculogram references. Therefore, this method could be particularly useful in brain–computer interface applications using headband-type wearable EEG devices with a few frontal EEG channels. Second, this method could estimate the ranges of eye blink artifacts accurately. Our experimental results demonstrated that the artifact range estimated using our method was more accurate than that from the conventional methods, and thus, the overall accuracy of detecting epochs contaminated by eye blink artifacts was markedly increased as compared to conventional methods. The MATLAB package of our library source codes and sample data, named Eyeblink Master, is open for free download.

Development of a Wearable Electroencephalographic Device for Anxiety Monitoring1

Anxiety disorders are the most common mental illness in the U.S. affecting nearly 40 10 adults (18% of the U.S. Population). It has been shown that patients with recurring anxiety tend to be more suicidal and prone to depression [1,2]. Anxiety has also been proven to be an impeding factor in the rehabilitation time of subjects suffering from various diseases ranging from pulmonary afflictions [3] to cancer [4]. Detection of anxiety is commonly done through various tests conducted by psychologists [5], which are time consuming and not necessarily accurate. Recently, electroencephalographic (EEG) data have been shown to be useful in the case of most anxiety patients to detect the levels of anxiety [6–8]. Two of the key areas of the brain that are known to be affected by patients suffering from social anxiety are the occipital and frontal lobes [9,10]. EEG data are best explained through rhythms in the frequency domain: delta (1–4 Hz), theta (4–7 Hz), alpha (7–12 Hz), beta (12–30 Hz), and gamma (>30 Hz). Research in the field has shown that changes in the alpha feedback are commonly seen in high anxiety and neurotic patients [11,12] and alpha EEG biofeedback is commonly used in the management of anxiety [13,14]. It has also been suggested that anxiety can be detected through analysis of alpha-theta and theta waves of the EEG signals [15,16] while theta waves are most commonly seen in the case of animal experimentation of anxiety and fear [17]. Considering the importance of real-time observation and management of anxiety states in patients, we developed a wearable EEG device for anxiety monitoring and investigated the influence of induced anxiety on the responses of the brainwaves obtained from the occipital lobes.

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<h3>Abstract</h3> Learning-related attention is one of the most important factors influencing learning. While technologies have enabled the automatic detection of students’ attention levels, the detected states might fail to be learning-related if students did not attend learning tasks (e.g., the attention level of a student who reads comics secretly during classroom learning). This phenomenon poses challenges to the practical application, especially in the primary school stage, which is crucial for students to set up learning attitudes/strategies. Inspired by the emerging inter-person perspective in neuroscience, we proposed an inter-brain attention coupling method to detect learning-related attention. Our method is based on the premise that learning-related attention should follow the structures of course contents, which is reflected in the attention dynamics shared across students. We hypothesized that one’s level of learning-related attention could be detected in the inter-brain attention coupling, which is defined as the degree to which an individual student’s attention dynamics match the attention dynamics averaged across classmates. To test this idea, wearable EEG devices were used to monitor students’ attention levels in a class of primary school students during classroom learning. We found that one’s inter-brain attention coupling was positively correlated with academic performance: higher performances are associated with higher coupling to the class-average attention dynamics. No significant correlation was found between students’ attention levels averaged within the individual and their academic performances. These results demonstrated the value of inter-brain attention coupling in assessing primary school students’ learning process. We argued that inter-person coupling analysis could be useful in monitoring learning-related attention in real-world educational contexts. <h3>Highlights</h3> Wearable EEG devices were used to monitor students’ attention levels in a class of primary school students during classroom learning. A better coupling for one’s attention dynamics to the class-average attention dynamics was associated with a better academic performance. One’s inter-brain attention coupling to class-average attention dynamics during lectures outperformed as an indicator of the learning process compared with the value of attention levels. The present finding suggests that students’ learning-related attention dynamics can be detected from an inter-person perspective, which will be useful for ‘wild’ educational applications. <h3>Graphic Abstract</h3>