Browsing by Author "Singh, Avinash Kumar"

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A Simple Communication System based on Brain Computer Interface
(2015)
Singh, Avinash Kumar
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Wang, Yu-Kai
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King, Jun-Tai
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Lin, Chin-Teng
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Ko, Li-Wei
The current study presents one Brain Computer Interface (BCI) based communication system in which the intentional eye blink were extracted from the single channel EEG data. This system could be useful for the sufferers of motor disease, locked in syndrome, and paralysis. In particular, one new Human Computer Interface (HCI) can also benefit healthy users. To detect the intentional eye blinking in real-time, the score was calculated from delta, theta and gamma power band from the brain dynamics acquired from single channel through NeuroSky Headset wirelessly. The soft and hard blink represent \'0\' and \'1\' in this system respectively and formed four continuous bit string which map to the pre-defined text in this system. The mapped text was convert into speech and send to the speaker. The experimental results shows that this system can provide an accurate and convenient way to communicate through the brain dynamics.
A Wireless Multifunctional SSVEP-Based Brain-Computer Interface Assistive System
(2019-09-01)
Lin, Chin-Teng
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Chiu, Ching-Yu
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Singh, Avinash Kumar
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King, Jung-Tai
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Ko, Li-Wei
Several kinds of brain-computer interface (BCI) systems have been proposed to compensate for the lack of medical technology for assisting patients who lose the ability to use motor functions to communicate with the outside world. However, most of the proposed systems are limited by their nonportability, impracticality, and inconvenience because of the adoption of wired or invasive electroencephalography acquisition devices. Another common limitation is the shortage of functions provided because of the difficulty of integrating multiple functions into one BCI system. In this paper, we propose a wireless, non-invasive and multifunctional assistive system which integrates steady state visually evoked potential-based BCI and a robotic arm to assist patients to feed themselves. Patients are able to control the robotic arm via the BCI to serve themselves food. Three other functions: 1) video entertainment; 2) video calling; and 3) active interaction are also integrated. This is achieved by designing a functional menu and integrating multiple subsystems. A refinement decision-making mechanism is incorporated to ensure the accuracy and applicability of the system. Fifteen participants were recruited to validate the usability and performance of the system. The averaged accuracy and information transfer rate achieved is 90.91% and 24.94 bit per min, respectively. The feedback from the participants demonstrates that this assistive system is able to significantly improve the quality of daily life.
Detecting Visuo-Haptic Mismatches in Virtual Reality using the Prediction Error Negativity of Event-Related Brain Potentials
(2019-01-01)
Gehrke, Lukas
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Akman, Sezen
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Lopes, Pedro
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Chen, Albert
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Singh, Avinash Kumar
Designing immersion is the key challenge in virtual reality; this challenge has driven advancements in displays, rendering and recently, haptics. To increase our sense of physical immersion, for instance, vibrotactile gloves render the sense of touching, while electrical muscle stimulation (EMS) renders forces. Unfortunately, the established metric to assess the effectiveness of haptic devices relies on the user's subjective interpretation of unspecific, yet standardized, questions. Here, we explore a new approach to detect a conflict in visuo-haptic integration (e.g., inadequate haptic feedback based on poorly configured collision detection) using electroencephalography (EEG). We propose analyzing event-related potentials (ERPs) during interaction with virtual objects. In our study, participants touched virtual objects in three conditions and received either no haptic feedback, vibration, or vibration and EMS feedback. To provoke a brain response in unrealistic VR interaction, we also presented the feedback prematurely in 25% of the trials. We found that the early negativity component of the ERP (so called prediction error) was more pronounced in the mismatch trials, indicating we successfully detected haptic conflicts using our technique. Our results are a first step towards using ERPs to automatically detect visuo-haptic mismatches in VR, such as those that can cause a loss of the user's immersion.
Forehead EEG in support of Future Feasible Personal Healthcare Solutions: Sleep Management, Headache Prevention, and Depression Treatment
(2017-01-01)
Lin, Chin-Teng
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Chuang, Chun-Hsiang
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Cao, Zehong
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Singh, Avinash Kumar
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Hung, Chih-Sheng
There are current limitations in the recording technologies for measuring EEG activity in clinical and experimental applications. Acquisition systems involving wet electrodes are time-consuming and uncomfortable for the user. Furthermore, dehydration of the gel affects the quality of the acquired data and reliability of long-term monitoring. As a result, dry electrodes may be used to facilitate the transition from neuroscience research or clinical practice to real-life applications. EEG signals can be easily obtained using dry electrodes on the forehead, which provides extensive information concerning various cognitive dysfunctions and disorders. This paper presents the usefulness of the forehead EEG with advanced sensing technology and signal processing algorithms to support people with healthcare needs, such as monitoring sleep, predicting headaches, and treating depression. The proposed system for evaluating sleep quality is capable of identifying five sleep stages to track nightly sleep patterns. Additionally, people with episodic migraines can be notified of an imminent migraine headache hours in advance through monitoring forehead EEG dynamics. The depression treatment screening system can predict the efficacy of rapid antidepressant agents. It is evident that frontal EEG activity is critically involved in sleep management, headache prevention, and depression treatment. The use of dry electrodes on the forehead allows for easy and rapid monitoring on an everyday basis. The advances in EEG recording and analysis ensure a promising future in support of personal healthcare solutions.
Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering
(2018-01-01)
Lin, Chin-Teng
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Huang, Chih-Sheng
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Yang, Wen-Yu
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Singh, Avinash Kumar
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Chuang, Chun-Hsiang
Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.
Visual Appearance Modulates Prediction Error in Virtual Reality
(2018-01-01)
Singh, Avinash Kumar
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Chen, Hsiang-Ting
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Cheng, Yu-Feng
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King, Jung-Tai
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Ko, Li-Wei
Different rendering styles induce different levels of agency and user behaviors in virtual reality environments. We applied an electroencephalogram-based approach to investigate how the rendering style of the users' hands affects behavioral and cognitive responses. To this end, we introduced prediction errors due to cognitive conflicts during a 3-D object selection task by manipulating the selection distance of the target object. The results showed that, for participants with high behavioral inhibition scores, the amplitude of the negative event-related potential at approximately 50-250 ms correlated with the realism of the virtual hands. Concurring with the uncanny valley theory, these findings suggest that the more realistic the representation of the user's hand is, the more sensitive the user becomes toward subtle errors, such as tracking inaccuracies.
Voice Navigation Effects on Real-World Lane Change Driving Analysis Using an Electroencephalogram
(2018-01-01)
Lin, Chin-Teng
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King, Jung-Tai
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Singh, Avinash Kumar
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Gupta, Akshansh
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Ma, Zhenyuan
Improving the degree of assistance given by in-car navigation systems is an important issue for the safety of both drivers and passengers. There is a vast body of research that assesses the usability and interfaces of the existing navigation systems but very few investigations study the impact on the brain activity based on navigation-based driving. In this paper, a real-world experiment is designed to acquire the electroencephalography (EEG) and in-car information to analyze the dynamic brain activity while the driver is performing the lane-changing task based on the auditory instructions from an in-car navigation system. The results show that auditory cues can influence the speed and increase the frontal EEG delta and beta power, which is related to motor preparation and decision making during a lane change. However, there were no significant results on the alpha power. A better lane-change assessment can be obtained using specific vehicle information (lateral acceleration and heading angle) with EEG features for future naturalized driving study.