Papers by Keyword: EEG

Paper TitlePage

Authors: Yan Zhao Wang, Gui Hao Liang, Jian Zhang
Abstract: The following description illustrates the implementation of an EEG-based Brain Computer Interface (BCI) for control of robotic arm utilizing electroencephalography (EEG) rhythms. The signals are collected with a headset witch denoises and classifies sampling signals adopting Fast Fourier Transform (FFT) and Kalman filter. These signals are deciphered to signals that can be eventually understood by certain machine, and then transmitted to a single-chip system by employing Bluetooth Module. Signals received by end-user machine will be compiled into control commands by MCU system and these commands are used to control actions of a robotic arm. Keywords: electroencephalogram;EEG;brain computer interface;BCI;MCU;robotic arm
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Authors: Yong Xiang Li, Nian Qiang Li, Yang Liu
Abstract: Most of the electroencephalograph (EEG) acquisition systems use group of high precision electrical signal amplifier to record and analysis. But the huge input circuit and testing time is difficult to application for portable devices. In this paper, a new design of portable EEG signal acquisition system is designed. This paper presents an effective chip (ADS1299) implementation of an eight-channel EEG signal acquisition. Compared with the existing system, this design greatly simplifies the front-end circuits and improves the common mode rejection ratio (CMRR). The system has the features of high integration density, good flexibility and practicability. It is in line with the development trend of modern EEG, and meets the new requirements of modern EEG.
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Authors: Zhuo Chen Ge, Ren Jun Liu, Ran Hao Lu, Cheng Fan Lin, Run Jing Zhang, Zi Yi Pan
Abstract: A portable mind wave monitor which is a special made headband for intellectual disabled children is proposed. The headband could make the children easier to be understood by the teachers and researchers. The EEG (electroencephalogram-graph) sensor, live camera, microphone and a series of sensors is consisted in the headband. With the Wi-Fi or 3G network, the headband could connect to server though the internet at any time. The teachers and researchers can access on the website or apps on smartphone to manage the children daily testing data. Intellectual disable children always have low self-control ability, limited language ability and self-associated complications, and this system meets the special needs for the teachers and researchers. This headband enable teachers and parents have the same perspective as the intellectual disabled children.
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Authors: Gui Xin Zhang, Ping Dong Wu, Man Ling Huang, Shuang Liu
Abstract: To expand the visual field of the 3D display screen, we propose a 3D visual image location controller based on the visual evoked potential (VEP). The controller evokes potential information by visual simulator. It constructs high quality data collection system by an EEG amplifier, then abstracts the characteristics of the EEG signal by signal processing algorithm and controls the movement of image on the 3D display screen by transforming them into controlling signal. This research can expand the visual field of the 3D display screen.
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Authors: Shao Zeng Yang, Jian Hua Zhang
Abstract: Operator functional state (OFS) is defined as the time-variable ability that an operator completes his/her assigned tasks. To evaluate the OFS in safety-critical human-machine systems, it is modeled by using the Wang-Mendel-based fuzzy system paradigm in this paper. The fuzzy model is constructed to correlate three EEG features (as model inputs) to the human-machine system performance (as model output). To derive a fuzzy model for real-time OFS assessment, the Gaussian membership function membership crossover point membership grade δ is found to be an essential parameter that controls the robustness of data-driven fuzzy models. The fuzzy models with different δ are applied to the OFS fuzzy modeling. The results have demonstrated that an appropriate value of δ can be selected to derive robust fuzzy models. Compare with the results obtained by fuzzy models based on symmetric Gaussian membership functions, the new approach based on asymmetric Gaussian membership function leads to considerably improved robustness performance.
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Authors: Yi Hung Liu, Jui Tsung Weng, Han Pang Huang, Jyh Tong Teng
Abstract: P300 speller is a well-known brain-computer interface (BCI), which allows patients with severe motor disabilities to spell words through the recognition on patients’ brain activity measured by electroencephalography (EEG). The brain-activity recognition is essentially a task of detecting of P300 responses in EEG signals. Support vector machine (SVM) has been a widely-used P300 detector in existing works. However, SVM is computationally expensive, greatly reducing the usability of the speller BCI for practical use. To address this issue, we propose in this paper a novel P300 detector, which is based on the kernel principal component analysis (KPCA). The proposed detector has a lower computational complexity, and can measure the belongingness of an input EEG to P300 class by the construction of EEG in nonlinear eigenspaces. Results carried out on subjects show that the proposed method is able to significantly shorten offline training sessions of the speller BCI while achieving high online P300-detection accuracy.
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Authors: Hong Yan Zhang, Zhi Jun Qiua, Xin Tian
Abstract: 16-channel EEG data during intermittent episodes of epilepsy is recoded and analyzed to find the lesions source for temporal lobe epilepsy by Granger causality. And the analysis of EEG with the lesions of temporal lobe epilepsy and non-lesions area of relationship between cause and effects is focused on, exploring temporal lobe epilepsy with lesions in other regions of the information transduction relationship for the clinical determination of temporal lobe epileptic focus to provide a theoretical the support. There are 8 cases patients which 6 cases patients of left-temporal lobe epilepsy and 2 cases patients of right-temporal lobe epilepsy in this work, Sampling frequency fs=200Hz,time t=20s (Sampling points N=4000). Granger causality is used to direction of the information transduction between each channel of the EEG signals. Autoregressive methods of EEG analysis for multi-channel data and determination of the coefficient of the matrix A, and proportion of operations on the matrix A is applied to get the result of matrix I which can reflect the causal relationship between each channel. The result shows that Granger causality can test the location of the lesions source and the areas that the information was reached. The results are consistent with the diagnosis of the EEG and CT. Granger causality not only can analyze the causal relationship of EEG, and it can calculate the multi-channel information transduction between the directions of EEG, which can provide support in the clinic for determine the source of seizure.
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Authors: Yi Yeh Lee, Aaron Raymond See, Shih Chung Chen, Chih Kuo Liang
Abstract: The purpose of this study was to utilize prefrontal EEG to discuss the theta EEG on the sleep quality of good and poor sleepers. Prefrontal EEG was chosen as it was positively correlated with reduced performance on neuropsychological tasks during total sleep deprivation. Hence, two test groups of ten volunteers were taken as test groups of good and poor sleepers. In addition, six tasks were performed using single channel forehead EEG. Results showed that audio stimulation provided the largest difference in theta amplitude between good and poor sleepers. Second, a large difference in the theta amplitude could be observed before and after the audio stimulation for poor sleepers. Third, it was also proven that prefrontal EEG could be conveniently applied for studying poor sleep qualities as it exhibited significant changes in the subject’s prefrontal EEG after biofeedback stimulation. In conclusion, the current research was able to provide significant differences between good and poor sleepers using prefrontal EEG through measuring and analyzing EEG theta wave.
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Authors: Prashanth Shyamkumar, Sechang Oh, Nilanjan Banerjee, Vijay K. Varadan
Abstract: A Remote Brain Machine Interface (RBMI) can be defined as a means to control a machine that is in a different geographical location than the user. Thus far, simulations for such interfaces using multiple channels of non-invasive EEG signals acquired through tethered systems have been used for control of vehicles in military and exploratory applications, and for ongoing research on RBMI controlled robotic surgery. However, simple applications of RBMI in home automation for the elderly, low cost assistive devices for the disabled, home security etc can be built using fewer and more portable sensor systems. As a case study, we have implemented such an interface using a smartphone for the RBMI. The system consists of a wearable Bluetooth-enabled head band with dry electrodes for EEG and EOG signals, a smartphone to collect and relay the data, a laptop with internet connectivity at a remote location to retrieve the data and generate control commands. In this paper, we describe the information architecture, the design of the wearable nanosensors and algorithms for control command generation based on EEG and EOG. A selected demonstration will be shown.
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Authors: Sutrisno Ibrahim, Sohaib Majzoub
Abstract: Epilepsy is type of neurological disorder characterized by recurrent seizures that may cause injury to self and others. The ability to predict seizure before its occurrence, so that counter measures are considered, would improve the quality of life of epileptic patients. This research work proposes an adaptive seizure prediction approach based on electroencephalography (EEG) signals analysis. We use cross-correlation to estimate synchronization between EEG channels. Abnormal synchronization between brain regions may reveal brain condition and functionality. Two EEG synchronization baselines, normal and pre-seizure, are used to continuously monitor sliding windows of EEG recording to predict the upcoming seizure. The two baselines are continuously updated using distance-based method based on the most recent prediction outcome. Up to 570 hours continuous EEG recording taken from CHB-MIT dataset is used for validating the proposed method. An overall of 84% sensitivity (46 out of 55 seizures are correctly predicted) and 63% specificity are achieved with one hour prediction horizon. The proposed method is suitable to be implemented in mobile or embedded device which has limited processing resources due to its simplicity.
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