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

An Attention-based Bidirectional LSTM Model for Continuous Cross-subject Estimation of Knee Joint Angle during Running from sEMG Signals

Running is an essential locomotion activity that plays a critical role in everyday life and exercise activities and may be impeded by joint disease and neurological impairments. Accurate and robust estimation of joint kinematics via surface electromyogram (sEMG) signals provides a human-machine interaction-based method that can be used to adequately control rehabilitation robots while performing complex movements such as running for motor function restoration in affected persons. To this end, this paper proposes a novel deep learning-based model (AM-BiLSTM) that integrates an attention mechanism (AM) and a bidirectional long short-term memory (BiLSTM) network. The proposed method was evaluated using knee joint kinematic and sEMG signals of fourteen subjects who performed running at 2 m/s speed. The proposed model’s generalizability was tested for within- and cross-subject scenarios and compared with standard LSTM and multi-layer perceptron (MLP) networks in terms of normalized root-mean-square error and correlation coefficient evaluation metrics. Based on the statistical tests, the proposed AM-BiLSTM model significantly outperformed the LSTM and MLP methods in both within- and cross-subject scenarios (p<0.05) and achieved state-of-the-art performance. © 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

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

Blind source extraction of heart sound signals from lung sound recordings exploiting periodicity of the heart sound

A novel approach for separating heart sound signals (HSSs) from lung sound recordings is presented. The approach is based on blind source extraction (BSE) with second-order statistics (SOS), which exploits the quasi-periodicity of the HSSs. The method is evaluated on both synthetic periodic signals of known period mixed with temporally white Gaussian noise (WGN) as well as on real quasi periodic HSSs mixed with lung sound signals (LSSs). Qualitative evaluation involving comparison of the power spectral densities (PSDs) of the extracted signals, by the proposed method and by the JADE algorithm, and that of the original signal is performed for the case of real data. Separation results confirm the utility of the proposed approach, although departure from strict periodicity may impact performance

Temporal modulation of the response of sensory fibers to paired-pulse stimulation

Multi-channel nerve cuff electrode arrays can provide sensory feedback to prosthesis users. To develop efficacious stimulation protocols, an understanding of the impact that spatio-temporal patterned stimulation can have on the response of sensory fibers is crucial. We used experimental and modelling methods to investigate the response of nerve fibers to paired-pulse stimulation. Nerve cuff electrode arrays were implanted for stimulation of the sciatic nerves of rats and the sensory compound action potentials were recorded from the L4 dorsal root. A model of the nerve cuff electrode array and sciatic nerve was also developed. The experimental and modelling results were compared. Experiments showed that it took 8 ms for the sensory fibers to completely recover from a conditioning stimulus, regardless of the relative position of the electrodes used for stimulation. The results demonstrate that the electrodes on the cuff cannot be considered independent. Additionally, at 120% of the threshold, there is a large overlap in the fibers that were activated by the different electrodes. If a stimulus paradigm considered the electrodes as independent, stimuli from the different electrodes would need to be interleaved, and the intervals between the stimuli should be greater than 8 ms

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

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

Greater intermanual transfer in the elderly suggests age-related bilateral motor cortex activation is compensatory

ABSTRACT. Hemispheric lateralization of movement control diminishes with age; whether this is compensatory or maladaptive is debated. The authors hypothesized that if compensatory, bilateral activation would lead to greater intermanual transfer in older subjects learning tasks that activate the cortex unilaterally in young adults. They studied 10 young and 14 older subjects, learning a unimanual visuomotor task comprising a feedforward phase, where there is unilateral cortical activation in young adults, and a feedback phase, which activates the cortex bilaterally in both age groups. Increased intermanual transfer was demonstrated in older subjects during feedforward learning, with no difference between groups during feedback learning. This finding is consistent with bilateral cortical activation being compensatory to maintain performance despite declining computational efficiency in neural networks