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A SOC Design of ORICA-based Highly Effective Real-time Multi-channel EEG System
(2019-01-01)
Ho, Yun-Lung
;
Huang, Yu-De
;
Wang, Kai-Yen
;
Fang, Wai-Chi
;
電子工程學系及電子研究所
Independent component analysis (ICA) has been wildly used to improve EEC based application such as brain computer interface (BCI). However, some well know ICA algorithm, such as Infomax ICA, suffering from the problem of convergence latency and make it hard to be apply on real-time application. This paper proposes a highly efficient chip implementation of multi-channel EEC real-time system based on online recursive independent component analysis algorithm (ORICA). The core size of the chip is 1.5525-mm(2) using 28nm CMOS technology. The EEC demonstration board will be implemented with the ORICA chip. The operation frequency and power consumption of the chip are 100 MHz and 17.9 mW respectively. The proposed chip was validated with a real-time circuit integrated system and the average correlation coefficient between simulations results and chip processing results is 0.958.
A SOC Design of ORICA-based Highly Effective Real-time Multi-channel EEG System
(2019-01-01)
Ho, Yun-Lung
;
Huang, Yu-De
;
Wang, Kai-Yen
;
Fang, Wai-Chi
;
電子工程學系及電子研究所
Independent component analysis (ICA) has been wildly used to improve EEC based application such as brain computer interface (BCI). However, some well know ICA algorithm, such as Infomax ICA, suffering from the problem of convergence latency and make it hard to be apply on real-time application. This paper proposes a highly efficient chip implementation of multi-channel EEC real-time system based on online recursive independent component analysis algorithm (ORICA). The core size of the chip is 1.5525-mm(2) using 28nm CMOS technology. The EEC demonstration board will be implemented with the ORICA chip. The operation frequency and power consumption of the chip are 100 MHz and 17.9 mW respectively. The proposed chip was validated with a real-time circuit integrated system and the average correlation coefficient between simulations results and chip processing results is 0.958.
Target Classification in a Novel SSVEP-RSVP Based BCI Gaming System
(2019-01-01)
Nayak, Tapsya
;
Ko, Li-Wei
;
Jung, Tzyy-Ping
;
Huang, Yufei
;
生物科技學系
Recently game-based brain-computer interface (BCI) systems using electroencephalography (EEG) has been gaining popularity, providing a sophisticated experience to its users. Here we present such a novel hybrid system based on rapid serial visual presentation (RSVP) in conjunction with steady-state visual evoked potentials (SSVEP). Based on a matching computer game Jewel Quest a game is designed wherein a sequence of jewel images containing rare targets (< 3%) in an RSVP paradigm is presented on a display at four distinct locations each flickering at different rates (4, 5, 6 and 7 Hz). A score is awarded upon successful detection of target image from neural signals. During real-time implementation to achieve higher classification speeds, EEG signals were epoched at the onset of each image, creating a high degree of class overlap and imbalance. Given these challenges in our EEG datasets, we present classifiers that can classify single-trial EEG epochs at the onset of target image presentation accurately. Initial results from 14 subjects indicate Hidden Markov Model (HMM) with Dirichlet emission probabilities provide similar to 1% higher, on average, the area under the precision-recall curve (AUC-PR) compared to the ensemble technique Bagging, commonly used to handle class imbalance.
Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)
(2018-04-01)
Wu, Dongrui
;
King, Jung-Tai
;
Chuang, Chun-Hsiang
;
Lin, Chin-Teng
;
Jung, Tzyy-Ping
Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77%, and at the same time increase the correlation to the true response speed by 19.39-86.47%.
A Visual Attention Monitor Based on Steady-State Visual Evoked Potential
(2016-03)
Lee, Yi-Chieh
;
Lin, Wen-Chieh
;
Cherng, Fu-Yin
;
Ko, Li-Wei
;
生物科技學系
Attention detection is important for many applications. Automatic determination of users\' visual attention state is challenging because attention involves numerous complex and internal human cognitive functions. Behavioral observations, such as eye gaze or response to external stimuli, can provide clues for users\' visual attention state; however, users\' cognitive state cannot be easily known. Conventional electroencephalography-based methods detect attention by observing the dynamic changes in the frontal lobe of the brain, especially in the anterior cingulate cortex (ACC). However, that area in the brain is associated with many functions, some of which correlate with conscious experience but are not directly related to attention. In this paper, we design an attention monitoring system to detect whether the brain experiences a visual stimulus consciously. Our experiments verified the feasibility of our design, and the average classification rate ranged from 72% to 82%.
Fuzzy Integral With Particle Swarm Optimization for a Motor-Imagery-Based Brain-Computer Interface
(2017-02)
Wu, Shang-Lin
;
Liu, Yu-Ting
;
Hsieh, Tsung-Yu
;
Lin, Yang-Yin
;
Chen, Chih-Yu
A brain-computer interface (BCI) system using electroencephalography signals provides a convenient means of communication between the human brain and a computer. Motor imagery (MI), in which motor actions are mentally rehearsed without engaging in actual physical execution, has been widely used as a major BCI approach. One robust algorithm that can successfully cope with the individual differences in MI-related rhythmic patterns is to create diverse ensemble classifiers using the subband common spatial pattern (SBCSP) method. To aggregate outputs of ensemble members, this study uses fuzzy integral with particle swarm optimization (PSO), which can regulate subject-specific parameters for the assignment of optimal confidence levels for classifiers. The proposed system combining SBCSP, fuzzy integral, and PSO exhibits robust performance for offline single-trial classification of MI and real-time control of a robotic arm using MI. This paper represents the first attempt to utilize fuzzy fusion technique to attack the individual differences problem of MI applications in real-world noisy environments. The results of this study demonstrate the practical feasibility of implementing the proposed method for real-world applications.
An Inflatable and Wearable Wireless System for Making 32-Channel Electroencephalogram Measurements
(2016-07)
Yu, Yi-Hsin
;
Lu, Shao-Wei
;
Chuang, Chun-Hsiang
;
King, Jung-Tai
;
Chang, Che-Lun
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.
Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network
(2016-02)
Liu, Yu-Ting
;
Lin, Yang-Yin
;
Wu, Shang-Lin
;
Chuang, Chun-Hsiang
;
Lin, Chin-Teng
This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.
A Motor Imagery Based Brain-Computer Interface System via Swarm-Optimized Fuzzy Integral and Its Application
(2016)
Wu, Shang-Lin
;
Liu, Yu-Ting
;
Chou, Kuang-Pen
;
Lin, Yang-Yin
;
Lu, Jie
A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications.
Design of the Multi-channel Electroencephalography-based Brain-Computer Interface with Novel Dry Sensors
(2012)
Wu, Shang-Lin
;
Liao, Lun-De
;
Liou, Chang-Hong
;
Chen, Shi-An
;
Ko, Li-Wei
The traditional brain-computer interface (BCI) system measures the electroencephalography (EEG) signals by the wet sensors with the conductive gel and skin preparation processes. To overcome the limitations of traditional BCI system with conventional wet sensors, a wireless and wearable multi-channel EEG-based BCI system is proposed in this study, including the wireless EEG data acquisition device, dry spring-loaded sensors, a size-adjustable soft cap. The dry spring-loaded sensors are made of metal conductors, which can measure the EEG signals without skin preparation and conductive gel. In addition, the proposed system provides a size-adjustable soft cap that can be used to fit user's head properly. Indeed, the results are shown that the proposed system can properly and effectively measure the EEG signals with the developed cap and sensors, even under movement. In words, the developed wireless and wearable BCI system is able to be used in cognitive neuroscience applications.

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