In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attention. This study tries to develop a SSVEP based BCI system that can control a wheel...In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attention. This study tries to develop a SSVEP based BCI system that can control a wheelchair prototype in five different positions including stop position. In this study four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using Lab-VIEW. Four stimuli colors, green, red, blue and violet were used to investigate the color influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were segmented into 1 second window and features were extracted by using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass SVM, is used to classify SSVEP signals. During stimuli color comparison SSVEP with violet color showed higher accuracy than that with green, red and blue stimuli.展开更多
The eye events (eye blink, eyes close and eyes open) are usually considered as biological artifacts in the electroencephalographic (EEG) signal. One can con-trol his or her eye blink by proper training and hence can b...The eye events (eye blink, eyes close and eyes open) are usually considered as biological artifacts in the electroencephalographic (EEG) signal. One can con-trol his or her eye blink by proper training and hence can be used as a control signal in Brain Computer Interface (BCI) applications. Support vector ma-chines (SVM) in recent years proved to be the best classification tool. A comparison of SVM with the Artificial Neural Network (ANN) always provides fruitful results. A one-against-all SVM and a multi-layer ANN is trained to detect the eye events. A com-parison of both is made in this paper.展开更多
Brain-Computer Interfaces (BCI) are developed to help locked-in patients, who lose control of their bodies and are unable to perform simple tasks such as speech, locomotion, and can’t even effectively interact, with ...Brain-Computer Interfaces (BCI) are developed to help locked-in patients, who lose control of their bodies and are unable to perform simple tasks such as speech, locomotion, and can’t even effectively interact, with their environment. BCI shows promise in allowing these individuals to interact with a computer using EEG. A Brain Computer Interface is a communication system in which messages or commands that an indi-vidual sends to the external world do not pass through the brain’s normal output pathways of peripheral nerves and muscles. A system is created to allow individuals with motor disabili-ties to control the motion of the bed on which they are bedridden via BCI for drug delivery and other activities, with the help of eye motion and changes in the absolute power in alpha rhythms of an EEG signal of the patient.展开更多
文摘In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attention. This study tries to develop a SSVEP based BCI system that can control a wheelchair prototype in five different positions including stop position. In this study four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using Lab-VIEW. Four stimuli colors, green, red, blue and violet were used to investigate the color influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were segmented into 1 second window and features were extracted by using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass SVM, is used to classify SSVEP signals. During stimuli color comparison SSVEP with violet color showed higher accuracy than that with green, red and blue stimuli.
文摘The eye events (eye blink, eyes close and eyes open) are usually considered as biological artifacts in the electroencephalographic (EEG) signal. One can con-trol his or her eye blink by proper training and hence can be used as a control signal in Brain Computer Interface (BCI) applications. Support vector ma-chines (SVM) in recent years proved to be the best classification tool. A comparison of SVM with the Artificial Neural Network (ANN) always provides fruitful results. A one-against-all SVM and a multi-layer ANN is trained to detect the eye events. A com-parison of both is made in this paper.
文摘Brain-Computer Interfaces (BCI) are developed to help locked-in patients, who lose control of their bodies and are unable to perform simple tasks such as speech, locomotion, and can’t even effectively interact, with their environment. BCI shows promise in allowing these individuals to interact with a computer using EEG. A Brain Computer Interface is a communication system in which messages or commands that an indi-vidual sends to the external world do not pass through the brain’s normal output pathways of peripheral nerves and muscles. A system is created to allow individuals with motor disabili-ties to control the motion of the bed on which they are bedridden via BCI for drug delivery and other activities, with the help of eye motion and changes in the absolute power in alpha rhythms of an EEG signal of the patient.
