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This paper describes a robust method for heart beat detection from noisy electrocardiogram (ECG) signals. Generally, the QRS-complex of heart beat is extracted from the ECG using a threshold. However, in a noisy condition such a mobile and wearable bio-signal monitoring system, noise increases the incidence of misdetection and false detection of QRS-complex. To prevent incorrect detection, we introduce...
While scalp EEG/MEG source imaging have been extensively studied in the last two decades, the case of source localization from invasive measurements has resulted in few works to date. Yet there is a lot to gain from stereo-electroencephalographic (SEEG) recordings, providing high signal to noise ratio measurements of the explored brain structures. The SEEG setup consists in multi-contact electrodes...
Many proposed EEG-based brain-computer interfaces (BCIs) make use of visual stimuli to elicit steady-state visual evoked potentials (SSVEP), the frequency of which can be mapped to a computer input. However, such a control scheme can be ineffective if a user has no motor control over their eyes and cannot direct their gaze towards a flashing stimulus to generate such a signal. Tactile-based methods,...
Despite the prevalence of stroke-induced gait impairment due to foot drop, current rehabilitative practices to improve gait function are limited, and orthoses can be uncomfortable and do not provide long-lasting benefits. Therefore, novel modalities that may facilitate lasting neurological and functional improvements, such as brain-computer interfaces (BCIs), have been explored. In this article, we...
Feature extraction is a critical step in real-time spike sorting after a spike is detected. Features should be informative and noise insensitive for high classification accuracy. This paper describes a new feature extraction method that utilizes a feature denoising filter to improve noise immunity while preserving spike information. Six features were extracted from filtered spikes, including a newly...
An element design for receive array coils that decouples from the transmit coil without external active detuning is presented for magnetic resonance imaging (MRI) of mice. The array element uses a crisscrossed geometry on the return paths to reduce the current induced by the transmit coil. Without the need for an external active detune network, the proposed method simplifies the construction of MRI...
We introduce a fast iterative non-local shrinkage algorithm to recover MRI data from undersampled Fourier measurements. This approach is enabled by the reformulation of current non-local schemes as an alternating algorithm to minimize a global criterion. The proposed algorithm alternates between a non-local shrinkage step and a quadratic subproblem. The resulting algorithm is observed to be considerably...
Due to fundamental characteristics of MRI that limit scan speedup, sub-sampling techniques such as compressed sensing (CS) have been developed for rapid MRI. Current CS MRI approaches utilize sparsity of the image in the wavelet or other transform domains to speed-up acquisition. Another drawback of MRI is its poor signal-to-noise ratio (SNR), which is proportional to the image slice thickness. In...
This paper considers the potential of CMOS-based nanopore measurement systems for high-speed molecular recording and DNA sequencing in particular. As such it approximates the signal fidelity limitations of a CMOS-based nanopore readout channel and discusses the means by which throughput could be increased through the use of a parallel CMOS readout system.
Wearable EEG systems for continuous brain monitoring is an emergent technology that involves significant technical challenges. Some of these are related to the fact that these systems operate in conditions that are far less controllable with respect to interference and artifacts than is the case for conventional systems. Quantitative assessment of artifacts provides a mean for optimization with respect...
Regenerative peripheral nerve interfaces have been proposed as viable alternatives for the natural control and feel of robotic prosthetic limbs. We have developed a Regenerative Multi-electrode Interface (REMI) that guides re-growing axons through an electrode array deployed in the lumen of a nerve guide. While acute studies have shown the use of the REMI in the rat sciatic nerve, the quality of chronic...
The aim of the present study is to propose a subject-specific screening approach of High Density surface EMG (HD-sEMG) Probability Density Function (PDF) shape evolution in experimental conditions following a ramp exercise from 0% to 50% of the Maximum Voluntary Contraction (MVC) during 25 seconds of isometric contractions of the Biceps Brachii from six healthy subjects. This method uses High Order...
Magnetic resonance imaging is empowered by parallel reading, which reduces acquisition time dramatically. The time saved by parallelization can be used to increase image quality or to enable specialized scanning protocols in clinical and research environments. In small animals, the sizing constraints render the use of multi-channeled approaches even more necessary, as they help to improve the typically...
Magnetic Resonance Angiography (MRA) is a group of techniques based on Magnetic Resonance Imaging (MRI) to image blood vessels. Compressed Sensing (CS) is a mathematical framework to reconstruct MR images from sparse data to minimize the data acquisition time. Image spar-sity is the key in CS to reconstruct MR images. CS technique allows reconstruction from significantly fewer k-space samples as compared...
A new MR spectroscopic imaging method, called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation), has been recently proposed to enable highresolution metabolic imaging with good SNR. A key problem within the SPICE framework is image reconstruction from a very noisy and sparsely sampled dataset. This paper addresses this problem by integrating the low-rank model used in SPICE reconstruction...
Biosignal classification systems often have to deal with extraneous features, highly imbalanced datasets, and a low SNR. A robust feature selection/reduction method is a crucial step in this process. Sets of artificial data were generated to test a prototype EEG-based microsleep detection system, consisting of a combination of EEG and 2-s bursts of 15-Hz sinusoids of varied signal-to-noise ratios...
Electroencephalographic (EEG) data give a direct non-invasive measurement of neural brain activity. Nevertheless, the common assumption about EEG stationarity (timeinvariant process) is a strong limitation for understanding real behavior of underlying neural networks. Here, we propose an approach for finding networks of brain regions connected by functional associations (functional connectivity) that...
Non-invasive EEG recordings are subject to effects such as surface conduction, resulting in very low signal to noise ratio (SNR). The conventional approach of using signal averaging to improve the SNR cannot be used for single trial EEG estimation. As such, this paper proposes a beamforming based technique that can be used to improve the signal quality from a signal trial EEG measurement. Results...
Gradient adaptive step size adaptive filters have been widely used to adapt different biomedical application environments and obtain useful life signals from serious ambient noise and interferences. In order to further improve the signal-to-noise ratio (SNR) of the life signals, this paper presents a class of signed-gradient adaptive step size least mean square (LMS) adaptive filters. The proposed...
Single Particle Reconstruction (SPR) for Cryogenic Electron Microscopy (cryo-EM) aligns and averages the images extracted from micrographs to improve the Signal-to-Noise ratio (SNR). Outliers compromise the fidelity of the averaging. We propose a robust cross-correlation-like w-estimator for combating the effect of outliers on the average images in cryo-EM. The estimator accounts for the natural variation...
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