In stroke rehabilitation,rehabilitation equipments can help with the training.But traditional equipments are not convenient to carry,which limits patients to use related rehabilitation techniques.To solve this kind of...In stroke rehabilitation,rehabilitation equipments can help with the training.But traditional equipments are not convenient to carry,which limits patients to use related rehabilitation techniques.To solve this kind of problem,a new embedded rehabilitation system based on brain computer interface(BCI)is proposed in this paper.The system is based on motor imagery(MI)therapy,in which electroencephalogram(EEG)is evoked by grasping motor imageries of left and right hands,then collected by a wearable device.The EEG is transmitted to a Raspberry Pie processing unit through Bluetooth and decoded as the instructions to control the equipment extension.Users experience the limb movement through the visual feedback so as to achieve active rehabilitation.A pilot study shows that the user can control the movement of the rehabilitation equipment through his mind,and the equipment is convenient to carry.The study provides a new way to stroke rehabilitation.展开更多
FastICA是独立成分分析法(ICA)中的一种快速算法,因其收敛速度快而备受关注.本文将基于负熵判据的FastICA算法应用于运动想象脑电信号的识别中,并根据ICA算法的特点设计了数据处理的实验流程.针对2003年国际BCI竞赛data set III中通道...FastICA是独立成分分析法(ICA)中的一种快速算法,因其收敛速度快而备受关注.本文将基于负熵判据的FastICA算法应用于运动想象脑电信号的识别中,并根据ICA算法的特点设计了数据处理的实验流程.针对2003年国际BCI竞赛data set III中通道数较少的问题,提出一种通道扩维算法,在不增加采集电极数的情况下,可成倍地增加相似通道的数量,提供更丰富的脑电信息.将通道扩维与FastICA算法相融合应用于国际BCI竞赛数据的处理中,实验结果表明通道扩维算法提升了FastICA算法的分类准确率,同时FastICA算法处理信息速度较快的特点也弥补了通道扩维算法耗时较多的缺陷.展开更多
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
In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in...In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.展开更多
基金Supported by the National Natural Science Foundation of China(61671193)Science and Technology Program of Zhejiang Province(2018C04012,2017C33049)Science and Technology Platform Construction Project of Fujian Science and Technology Department(2015Y2001)
文摘In stroke rehabilitation,rehabilitation equipments can help with the training.But traditional equipments are not convenient to carry,which limits patients to use related rehabilitation techniques.To solve this kind of problem,a new embedded rehabilitation system based on brain computer interface(BCI)is proposed in this paper.The system is based on motor imagery(MI)therapy,in which electroencephalogram(EEG)is evoked by grasping motor imageries of left and right hands,then collected by a wearable device.The EEG is transmitted to a Raspberry Pie processing unit through Bluetooth and decoded as the instructions to control the equipment extension.Users experience the limb movement through the visual feedback so as to achieve active rehabilitation.A pilot study shows that the user can control the movement of the rehabilitation equipment through his mind,and the equipment is convenient to carry.The study provides a new way to stroke rehabilitation.
文摘FastICA是独立成分分析法(ICA)中的一种快速算法,因其收敛速度快而备受关注.本文将基于负熵判据的FastICA算法应用于运动想象脑电信号的识别中,并根据ICA算法的特点设计了数据处理的实验流程.针对2003年国际BCI竞赛data set III中通道数较少的问题,提出一种通道扩维算法,在不增加采集电极数的情况下,可成倍地增加相似通道的数量,提供更丰富的脑电信息.将通道扩维与FastICA算法相融合应用于国际BCI竞赛数据的处理中,实验结果表明通道扩维算法提升了FastICA算法的分类准确率,同时FastICA算法处理信息速度较快的特点也弥补了通道扩维算法耗时较多的缺陷.
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.
文摘In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.
