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Chronic effects of electrode implantation in the brain tissue alter the neural channel signal-to-noise ratio (SNR) over time. Variability of signal quality over time poses a difficult challenge in long-term decoding of neural signals for Brain Computer Interface (BCI). Specifically, all channels observed during a neural recording session may not be observed during the next recording session. This...
We have previously published a full report [25] comparing the performance of three automated electromyogram (EMG) decomposition algorithms. In our prior report, the primary measure of decomposition difficulty/challenge for each data record was the “Decomposability Index” of Florestal et al. [3]. This conference paper is intended to augment our prior work by providing companion results when the measure...
A new denoising technique for preprocessing of P300 and Slow Cortical Potential (SCP)-based Brain computer interface data is proposed. This new technique adaptively removes the superimposed noise using a modified version of spectral subtraction method. A better performance is achieved especially when less number of electrodes is used which accordingly reduce weight and consumed power for portable...
In this paper, a nonlinear independent component analysis (NICA) extraction method for brain signal based EEG-P300 are proposed. The performance of the proposed method is investigated through a comparison of well-known extraction methods (i.e., AAR, JADE, and SOBI algorithms). Finally, the promising results reported here reflect the considerable potential of EEG for the continuous classification of...
This work investigates the impact of the analogue front-end design (pre-amplifier, filter and converter) on spike sorting performance in neural interfaces. By examining key design parameters including the signal-to-noise ratio, bandwidth, filter type/order, data converter resolution and sampling rate, their sensitivity to spike sorting accuracy is assessed. This is applied to commonly used spike sorting...
Minimum energy combination (MEC) and canonical correlation analysis (CCA) are widely used for steady-state visual evoked potential (SSVEP) based brain computer interface (BCI), since both approaches have satisfactory performance. The purpose of this paper is to provide a guideline on choice of detection method, through comparison of the performance of the two approaches from simulation data and real...
A Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by direct control from decoding of brain activity. This paper deals with the P300-speller application that enables to write a text based on the oddball paradigm. To improve the ergonomics and minimize the cost of such a BCI, reducing the number of electrodes...
Recognition of brain states and subject's intention from electroencephalogram (EEG) is a challenging problem for braincomputer interaction. Signals recorded from each of EEG electrodes represent noisy spatio-temporal overlapping of activity arising from very diverse brain regions. However, un-mixing methods such as cortical current density (CCD) can be used for estimating activity of different brain...
In the paper we describe a method to extract the topography of an impressed current for our bounded electrical impedance tomography (bEIT) studies. The frequency of the impressed current is low (up to a few hundred Hz), and is buried in background EEG and other noise. For the development of the extraction method, special consideration is given to maximize the signal-to-noise ratio. The standard lock-in...
In this study we have investigated the classification of old myocardial infarction through the analysis of 192 lead body surface potential maps (BSPM). Following an analysis of the most prominent features based on a signal to noise ratio ranking criterion the top 6 features were selected. These features were subsequently used as inputs to a series of supervised classification models in the form of...
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