Steady-State movement related potentials for brain–computer interfacing

An approach for brain-computer interfacing (BCI) by analysis of steady-state movement related potentials (ssMRPs) produced during rhythmic finger movements is proposed in this paper. The neurological background of ssMRPs is briefly reviewed. Averaged ssMRPs represent the development of a lateralized rhythmic potential, and the energy of the EEG signals at the finger tapping frequency can be used for single-trial ssMRP classification. The proposed ssMRP-based BCI approach is tested using the classic Fisher's linear discriminant classifier. Moreover, the influence of the current source density transform on the performance of BCI system is investigated. The averaged correct classification rates (CCRs) as well as averaged information transfer rates (ITRs) for different sliding time windows are reported. Reliable single-trial classification rates of 88%-100% accuracy are achievable at relatively high ITRs. Furthermore, we have been able to achieve CCRs of up to 93% in classification of the ssMRPs recorded during imagined rhythmic finger movements. The merit of this approach is in the application of rhythmic cues for BCI, the relatively simple recording setup, and straightforward computations that make the real-time implementations plausible

Spatio-temporal feature extraction in sensory electroneurographic signals

The recording and analysis of peripheral neural signal can provide insight for various prosthetic and bioelectronics medicine applications. However, there are few studies that investigate how informative features can be extracted from population activity electroneurographic (ENG) signals. In this study, five feature extraction frameworks were implemented on sensory ENG datasets and their classification performance was compared. The datasets were collected in acute rat experiments where multi-channel nerve cuffs recorded from the sciatic nerve in response to proprioceptive stimulation of the hindlimb. A novel feature extraction framework, which incorporates spatio-temporal focus and dynamic time warping, achieved classification accuracies above 90% while keeping a low computational cost. This framework outperformed the remaining frameworks tested in this study and has improved the discrimination accuracy of the sensory signals. Thus, this study has extended the tools available to extract features from sensory population activity ENG signals. This article is part of the theme issue ‘Advanced neurotechnologies: translating innovation for health and well-being’

A novel semi-blind signal extraction approach incorporating parafac for the removal of eye-blink artifact from EEGs

In this paper, a novel iterative blind signal extraction (BSE) scheme for the removal of the eye-blink artifact from electroencephalogram (EEC) signals is proposed. In this method, in order to remove the artifact, the signal extraction algorithm is provided with a priori information, i.e., an estimation of the column of the mixing matrix corresponding to the eye- blink source. The a priori knowledge, namely the vector corresponding to the spatial distribution of the eye-blink factor, is identified by using the method of parallel factor analysis (PARAFAC). Hence, we call the BSE approach, semi- blind signal extraction (SBSE). The results demonstrate that the proposed algorithm effectively identifies and removes the eye-blink artifact from raw EEC measurements

Fetal Electrocardiogram Signal Modelling Using Genetic Algorithm

The fetal electrocardiogram signal (FECG) is a major tool in monitoring and diagnosis of the fetus high risk conditions and arrhythmias. This paper introduces an accurate mathematical model for fetal electrocardiogram based on a real template FECG signal. This is done after elimination of maternal electrocardiogram (MECG) signal interference measured during pregnancy. The parameters of the introduced model are optimized in terms of sum square error using a genetic algorithm (GA). Our precise fetal electrocardiogram model can be a benchmark for other prospective researches particularly in FECG extraction. ©2007 IEEE

Steady-state movement related potentials for brain computer interfacing

An approach for brain computer interfacing (BCI) by analysis of the steady-state movement related potentials (ssMRP) is proposed in this paper. The neurological background of the ssMRPs which are primarily studied by means of the averaged electroencephalogram (EEG) signals are briefly reviewed. A simple feature extraction method is suggested for single trial ssMRP processing. The proposed BCI paradigm is tested by using the Fishers linear discriminant (FLD) classifier. The novelty of this approach is mainly in the application of rhythmic cues for BCI, simple recording setup, and straightforward computations which make the real-time implementations plausible

A robust minimum variance beamforming approach for the removal of the eye-blink artifacts from EEGs

In this paper a novel scheme for the removal of eye-blink (EB) artifacts from electroencephalogram (EEG) signals based on the robust minimum variance beamformer (RMVB) is proposed. In this method, in order to remove the artifact, the RMVB is provided with a priori information, i.e., an estimation of the steering vector corresponding to the point source EB artifact. The artifact-removed EEGs are subsequently reconstructed by deflation. The a priori knowledge, namely the vector corresponding to the spatial distribution of the EB factor, is identified using a novel space-time-frequency-time/segment (STF-TS) model of EEGs, provided by a four-way parallel factor analysis (PARAFAC) approach. The results demonstrate that the proposed algorithm effectively identifies and removes the EB artifact from raw EEG measurements

Model-based control of individual finger movements for prosthetic hand function

The authors gratefully acknowledge the support of the Engineering and Physical Sciences Research Council (EP/M025977/1) and the National Institutes of Health (NIH5R01EB011615) in this research.Peer reviewedPostprin