A novel P300-based BCI system for words typing

Abstract

The conventional P300 Bel system for character spelling is typically composed of a paradigm that displays flashing characters and a classifier which identifies target characters. Typically a user has to type each character of a word at a time: this spelling process is slow and it can take several minutes to type an entire word. In this work, we propose a new word typing scheme by integrating a word suggestion mechanism via a dictionary search into the conventional P300-based speller. Our new P300-based word typing system consists of an initial character spelling paradigm, a smart dictionary unit to give suggestions of possible words, and the final word selection paradigm to select a word out of the suggestions. Our proposed methodology reduces typing time significantly and makes word typing more convenient. We have tested our system with four subjects and our results demonstrate an average words typing time of 1.66 minute, whereas the conventional took 2.9 minute for the same words.

A new parameter tuning approach for enhanced motor imagery EEG signal classification

“…The dimension of each particle is set to 3, where dimensions represent the lower cutoff frequency, upper cutoff frequency and filter order of the butterworth bandpass filter. The range of the dimensions were set as follows: lower cutoff frequency [0.50 Hz, 16.0 Hz], upper cutoff frequency [18 Hz,32 Hz], and filter order [1,30]. The frequency ranges were selected so as to include the frequencies of the mu and beta rhythms.…”

“…, where n denotes the n-th sample, C is the number of channels and T is the number of time points. The spatially filtered signal Zn can be obtained using (1), where Wcsp is the CSP spatial filter that is formed by selecting the first and last m columns of CSP matrix, W. The variance based CSP features of n-th sample is then extracted using (2), where is the i-th feature of the nth sample, and var( ) denotes the variance of j-th row of .…”

“…BCI research is increasingly gaining interest as it aims to re-establish independence and reduce social exclusion for people with disabilities. BCI systems have been explored for a wide range of applications such as communication control [12], movement control [20], environment control [5], neurorehabilitation [9,22,30,31,43], P300-based word typing system [1], biometric identification [18], and brain wave controlled robots and wheelchairs for the disabled people [23,28]. Biomedical engineering is the major focus and emphasis of current BCI research [9, 14-16, 21, 22, 30, 42, 44, 51, 53, 58].…”

CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI

“…BCI has numerous emerging applications such as applications for communication control [2,3], environment control [4], movement control [5,6] and neuro-rehabilitation [7][8][9]. Researchers have also concentrated on developing BCI systems such as P300-based word typing system [10], EEG controlled mouse [11], brain wave controlled robots [12] and brain wave controlled wheelchair for the disabled [13]. EEG signal has also been used for biometric identification [14].…”

A User-Friendly Dictionary-Supported SSVEP-based BCI Application

“…E.g. Akram et al integrated a word suggestion mechanism in order to reduce typing time into a conventional P300-based speller [2]. With the conventional speller an average word typing time of 2.9 min was achieved.…”