Optimizing the P300-based brain-computer interface: current status, limitations and future directions.
نویسندگان
چکیده
This paper summarizes the presentations and discussions at a workshop held during the Fourth International BCI Meeting charged with reviewing and evaluating the current state, limitations and future development of P300-based brain-computer interface (P300-BCI) systems. We reviewed such issues as potential users, recording methods, stimulus presentation paradigms, feature extraction and classification algorithms, and applications. A summary of the discussions and the panel's recommendations for each of these aspects are presented.
منابع مشابه
Control of a 2-DoF robotic arm using a P300-based brain-computer interface
In this study, a novel control algorithm, based on a P300-based brain-computer interface (BCI) is fully developed to control a 2-DoF robotic arm. Eight subjects including 5 men and 3 women perform a 2-dimensional target tracking in a simulated environment. Their EEG (Electroencephalography) signals from visual cortex are recorded and P300 components are extracted and evaluated to perform a real...
متن کاملP300 brain computer interface: current challenges and emerging trends
A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty yea...
متن کاملSelecting and Extracting Effective Features of SSVEP-based Brain-Computer Interface
User interfaces are always one of the most important applied and study fields of information technology. The development and expansion of cognitive science studies and functionalization of its tools such as BCI1, as well as popularization of methods such as SSVEP2 to stimulate brain waves, have led to using these techniques every day, especially in appropriate solutions for physically and menta...
متن کاملTemporal Feature Selection for Optimizing Spatial Filters in a P300 Brain-Computer Interface
For the creation of efficient and robust BrainComputer Interfaces (BCIs) based on the detection of eventrelated potentials (ERPs) in the electroencephalogram (EEG), spatial filtering has been shown as being an important step for feature extraction and reduction. Current spatial filtering methods for ERP enhancement typically consider a global approach by enhancing the signal on a predefined tim...
متن کاملEvaluation of the Hidden Markov Model for Detection of P300 in EEG Signals
Introduction: Evoked potentials arisen by stimulating the brain can be utilized as a communication tool between humans and machines. Most brain-computer interface (BCI) systems use the P300 component, which is an evoked potential. In this paper, we evaluate the use of the hidden Markov model (HMM) for detection of P300. Materials and Methods: The wavelet transforms, wavelet-enhanced indepen...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Journal of neural engineering
دوره 8 2 شماره
صفحات -
تاریخ انتشار 2011