A brain-computer interface(BCI)-based electric wheelchair control system was developed, which enables the users to move the wheelchair forward or backward, and turn left or right without any pre-learning. This control...A brain-computer interface(BCI)-based electric wheelchair control system was developed, which enables the users to move the wheelchair forward or backward, and turn left or right without any pre-learning. This control system makes use of the amplitude enhancement of alpha-wave blocking in electroencephalogram(EEG) when eyes close for more than 1 s to constitute a BCI for the switch control of wheelchair movements. The system was formed by BCI control panel, data acquisition, signal processing unit and interface control circuit. Eight volunteers participated in the wheelchair control experiments according to the preset routes. The experimental results show that the mean success control rate of all the subjects was 81.3%, with the highest reaching 93.7%. When one subject's triggering time was 2.8 s, i.e., the flashing time of each cycle light was 2.8 s, the average information transfer rate was 8.10 bit/min, with the highest reaching 12.54 bit/min.展开更多
The patients with brain diseases(e.g.,Stroke and Amyotrophic Lateral Sclerosis(ALS))are often affected by the injury of motor cortex,which causes a muscular weakness.For this reason,they require rehabilitation with co...The patients with brain diseases(e.g.,Stroke and Amyotrophic Lateral Sclerosis(ALS))are often affected by the injury of motor cortex,which causes a muscular weakness.For this reason,they require rehabilitation with continuous physiotherapy as these diseases can be eased within the initial stages of the symptoms.So far,the popular control system for robot-assisted rehabilitation devices is only of two types which consist of passive and active devices.However,if there is a control system that can directly detect the motor functions,it will induce neuroplasticity to facilitate early motor recovery.In this paper,the control system,which is a motor recovery system with the intent of rehabilitation,focuses on the hand organs and utilizes a brain-computer interface(BCI)technology.The final results depict that the brainwave detection for controlling pneumatic glove in real-time has an accuracy up to 82%.Moreover,the motor recovery system enables the feasibility of brainwave classification from the motor cortex with Artificial Neural Networks(ANN).The overall model performance reveals an accuracy up to 96.56%with sensitivity of 94.22%and specificity of 98.8%.Therefore,the proposed system increases the efficiency of the traditional device control system and tends to provide a better rehabilitation than the traditional physiotherapy alone.展开更多
Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented ...Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.展开更多
This paper presents the application of an effective electroencephalogram(EEG)based brain-computer interface(BCI)for controlling a remote car in a practical environment.The BCI uses the motor imaginary to translate the...This paper presents the application of an effective electroencephalogram(EEG)based brain-computer interface(BCI)for controlling a remote car in a practical environment.The BCI uses the motor imaginary to translate the subject’s motor intention into a control signal through classifying EEG patterns of different imaginary tasks.The system is composed of a remote car,a digital signal processor and a wireless data transfer module.The performance of the BCI was found to be robust to distract motor imaginary in the remote car controlling and need a short training time.The experimental results indicate that the successful ternary-control by using motor imaginry may be practicable in an uncontrolled environment.展开更多
The tactile P300 brain-computer interface( BCI) is related to the somatosensory perception and response of the human brain,and is different from visual or audio BCIs. Recently,several studies focused on the tactile st...The tactile P300 brain-computer interface( BCI) is related to the somatosensory perception and response of the human brain,and is different from visual or audio BCIs. Recently,several studies focused on the tactile stimuli delivered to different parts of the human body. Most of these stimuli were symmetrically bilateral.Only a fewstudies explored the influence of tactile stimuli laterality.In the current study,we extensively tested the performance of a vibrotactile BCI system using ipsilateral stimuli and bilateral stimuli.Two vibrotactile P300-based paradigms were tested. The target stimuli were located on the left and right forearms for the left forearm and right forearm( LFRF) paradigm,and on the left forearm and calf for the left forearm and left calf( LFLC)paradigm. Ten healthy subjects participated in this study. Our experiments and analysis showed that the bilateral paradigm( LFRF) elicited larger P300 amplitude and achieved significantly higher classification accuracy than the ipsilateral paradigm( LFLC). However, both paradigms achieved classification accuracies higher than 70% after the completion of several trials on average,which was usually regarded as the minimum accuracy level required for BCI system to be deemed useful.展开更多
As a non-invasive neurophysiologieal index for brain-computer interface （BCI）, electroencephalogram （EEG） attracts much attention at present. In order to have a portable BCI, a simple and efficient pre-amplifier i...As a non-invasive neurophysiologieal index for brain-computer interface （BCI）, electroencephalogram （EEG） attracts much attention at present. In order to have a portable BCI, a simple and efficient pre-amplifier is crucial in practice. In this work, a preamplifier based on the characteristics of EEG signals is designed, which consists of a highly symmetrical input stage, low-pass filter, 50 Hz notch filter and a post amplifier. A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment. The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.展开更多
基金Supported by the National Natural Science Foundation of China(No.81222021,No.30970875,No.90920015,No.61172008 and No.81171423)National Key Technology Research and Development Program of the Ministry of Science and Technology of China(No.2012BAI34B02)Program for New Century Excellent Talents in University of the Ministry of Education of China(No.NCET-10-0618)
文摘A brain-computer interface(BCI)-based electric wheelchair control system was developed, which enables the users to move the wheelchair forward or backward, and turn left or right without any pre-learning. This control system makes use of the amplitude enhancement of alpha-wave blocking in electroencephalogram(EEG) when eyes close for more than 1 s to constitute a BCI for the switch control of wheelchair movements. The system was formed by BCI control panel, data acquisition, signal processing unit and interface control circuit. Eight volunteers participated in the wheelchair control experiments according to the preset routes. The experimental results show that the mean success control rate of all the subjects was 81.3%, with the highest reaching 93.7%. When one subject's triggering time was 2.8 s, i.e., the flashing time of each cycle light was 2.8 s, the average information transfer rate was 8.10 bit/min, with the highest reaching 12.54 bit/min.
基金the Declaration of Helsinki,and the protocol was approved by the Ethics Committee of Suranaree University of Technology(License EC-61-14 COA No.16/2561)the Thailand Research Fund through the RoyalGolden Jubilee Ph.D.Program(Grant No.PHD/0148/2557).
文摘The patients with brain diseases(e.g.,Stroke and Amyotrophic Lateral Sclerosis(ALS))are often affected by the injury of motor cortex,which causes a muscular weakness.For this reason,they require rehabilitation with continuous physiotherapy as these diseases can be eased within the initial stages of the symptoms.So far,the popular control system for robot-assisted rehabilitation devices is only of two types which consist of passive and active devices.However,if there is a control system that can directly detect the motor functions,it will induce neuroplasticity to facilitate early motor recovery.In this paper,the control system,which is a motor recovery system with the intent of rehabilitation,focuses on the hand organs and utilizes a brain-computer interface(BCI)technology.The final results depict that the brainwave detection for controlling pneumatic glove in real-time has an accuracy up to 82%.Moreover,the motor recovery system enables the feasibility of brainwave classification from the motor cortex with Artificial Neural Networks(ANN).The overall model performance reveals an accuracy up to 96.56%with sensitivity of 94.22%and specificity of 98.8%.Therefore,the proposed system increases the efficiency of the traditional device control system and tends to provide a better rehabilitation than the traditional physiotherapy alone.
基金Supported by the National Natural Science Foundation of China (No. 30570485)the Shanghai "Chen Guang" Project (No. 09CG69).
文摘Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.
基金National Natural Science Foundation of China(No.61071087,No.60970062)Natural Science Foundation of Shandong Province(No.ZR2011FM018)
文摘This paper presents the application of an effective electroencephalogram(EEG)based brain-computer interface(BCI)for controlling a remote car in a practical environment.The BCI uses the motor imaginary to translate the subject’s motor intention into a control signal through classifying EEG patterns of different imaginary tasks.The system is composed of a remote car,a digital signal processor and a wireless data transfer module.The performance of the BCI was found to be robust to distract motor imaginary in the remote car controlling and need a short training time.The experimental results indicate that the successful ternary-control by using motor imaginry may be practicable in an uncontrolled environment.
基金National Key Research and Development Program,China(No.2017YFB13003002)National Natural Science Foundation of China(Nos.61573142,61773164,91420302)Programme of Introducing Talents of Discipline to Universities(the 111 Project)(No.B17017)
文摘The tactile P300 brain-computer interface( BCI) is related to the somatosensory perception and response of the human brain,and is different from visual or audio BCIs. Recently,several studies focused on the tactile stimuli delivered to different parts of the human body. Most of these stimuli were symmetrically bilateral.Only a fewstudies explored the influence of tactile stimuli laterality.In the current study,we extensively tested the performance of a vibrotactile BCI system using ipsilateral stimuli and bilateral stimuli.Two vibrotactile P300-based paradigms were tested. The target stimuli were located on the left and right forearms for the left forearm and right forearm( LFRF) paradigm,and on the left forearm and calf for the left forearm and left calf( LFLC)paradigm. Ten healthy subjects participated in this study. Our experiments and analysis showed that the bilateral paradigm( LFRF) elicited larger P300 amplitude and achieved significantly higher classification accuracy than the ipsilateral paradigm( LFLC). However, both paradigms achieved classification accuracies higher than 70% after the completion of several trials on average,which was usually regarded as the minimum accuracy level required for BCI system to be deemed useful.
基金supported by the National Natural Science Foundation of China under Grant No. 60571019the University of Electronic Science and Technology of China Youth Foundation under Grant No. L08010901JX0772.
文摘As a non-invasive neurophysiologieal index for brain-computer interface （BCI）, electroencephalogram （EEG） attracts much attention at present. In order to have a portable BCI, a simple and efficient pre-amplifier is crucial in practice. In this work, a preamplifier based on the characteristics of EEG signals is designed, which consists of a highly symmetrical input stage, low-pass filter, 50 Hz notch filter and a post amplifier. A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment. The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.
