Recording Channels Research Articles

A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals

In the Motor Imagery (MI)-based Brain Computer Interface (BCI), users' intention is converted into a control signal through processing a specific pattern in brain signals reflecting motor characteristics. There are such restrictions as the limited size of the existing datasets and low signal to noise ratio in the classification of MI Electroencephalogram (EEG) signals. Machine learning (ML) methods, particularly Deep Learning (DL), have overcome these limitations relatively. In this study, three hybrid models were proposed to classify the EEG signal in the MI-based BCI. The proposed hybrid models consist of the convolutional neural networks (CNN) and the Long-Short Term Memory (LSTM). In the first model, the CNN with different number of convolutional-pooling blocks (from shallow to deep CNN) was examined; a two-block CNN model not affected by the vanishing gradient descent and yet able to extract desirable features employed; the second and third models contained pre-trained CNNs conducing to the exploration of more complex features. The transfer learning strategy and data augmentation methods were applied to overcome the limited size of the datasets by transferring learning from one model to another. This was achieved by employing two powerful pre-trained convolutional neural networks namely ResNet-50 and Inception-v3. The continuous wavelet transform (CWT) was used to generate images for the CNN. The performance of the proposed models was evaluated on the BCI Competition IV dataset 2a. The mean accuracy vlaues of 86%, 90%, and 92%, and mean Kappa values of 81%, 86%, and 88% were obtained for the hybrid neural network with the customized CNN, the hybrid neural network with ResNet-50 and the hybrid neural network with Inception-v3, respectively. Despite the promising performance of the three proposed models, the hybrid neural network with Inception-v3 outperformed the two other models. The best obtained result in the present study improved the previous best result in the literature by 7% in terms of classification accuracy. From the findings, it can be concluded that transfer learning based on a pre-trained CNN in combination with LSTM is a novel method in MI-based BCI. The study also has implications for the discrimination of motor imagery tasks in each EEG recording channel and in different brain regions which can reduce computational time in future works by only selecting the most effective channels.

Electroencephalography and analysis of functional brain activity

Electroencephalography (EEG) enables the recording and analysis of fast physiological processes in the cerebral cortex, which is currently virtually impossible for the majority of methods of functional brain examination. However, there is a certain stagnation in the EEG technology that requires some improvement in both methodological and technical approaches to EEG. Objective: to analyze the capacities of EEG and its role in current medical diagnostics. The majority of EEG machines have 8 to 32 recording channels and use the 10–20 electrode placement system. This results in loosing a substantial amount of information due to limited registration of some bioelectric activity and prevents using EEG data together with high-resolution neuroimaging. This issue can be addressed by EEG machines with high-density recording and multiple primary electroencephalographic sensors (at least 128), which ensures minimal loss of information at the first stage. However, such systems require an absolutely different approach to the analysis of EEG recordings. Larger amount of information (compared to conventional EEG) necessitates the implementation of various mathematical methods to analyze the recorded signals, as well as more extensive use of techniques for combining this data with findings of other diagnostic methods, primarily magnetic resonance imaging and functional magnetic resonance imaging. This will open new horizons for the investigation of bioelectric activity of the human brain.

Intracranial electrophysiological recordings from the human brain during memory tasks with pupillometry

Data comprise intracranial EEG (iEEG) brain activity represented by stereo EEG (sEEG) signals, recorded from over 100 electrode channels implanted in any one patient across various brain regions. The iEEG signals were recorded in epilepsy patients (N = 10) undergoing invasive monitoring and localization of seizures when they were performing a battery of four memory tasks lasting approx. 1 hour in total. Gaze tracking on the task computer screen with estimating the pupil size was also recorded together with behavioral performance. Each dataset comes from one patient with anatomical localization of each electrode contact. Metadata contains labels for the recording channels with behavioral events marked from all tasks, including timing of correct and incorrect vocalization of the remembered stimuli. The iEEG and the pupillometric signals are saved in BIDS data structure to facilitate efficient data sharing and analysis.

Highly Configurable 100 Channel Recording and Stimulating Integrated Circuit for Biomedical Experiments

This paper presents the design results of a 100-channel integrated circuit dedicated to various biomedical experiments requiring both electrical stimulation and recording ability. The main design motivation was to develop an architecture that would comprise not only the recording and stimulation, but would also block allowing to meet different experimental requirements. Therefore, both the controllability and programmability were prime concerns, as well as the main chip parameters uniformity. The recording stage allows one to set their parameters independently from channel to channel, i.e., the frequency bandwidth can be controlled in the (0.3 Hz–1 kHz)–(20 Hz–3 kHz) (slow signal path) or (0.3 Hz–1 kHz)–4.7 kHz (fast signal path) range, while the voltage gain can be set individually either to 43.5 dB or 52 dB. Importantly, thanks to in-pixel circuitry, main system parameters may be controlled individually allowing to mitigate the circuitry components spread, i.e., lower corner frequency can be tuned in the 54 dB range with approximately 5% precision, and the upper corner frequency spread is only 4.2%, while the voltage gain spread is only 0.62%. The current stimulator may also be controlled in the broad range (69 dB) with its current setting precision being no worse than 2.6%. The recording channels’ input-referred noise is equal to 8.5 µVRMS in the 10 Hz–4.7 kHz bandwidth. The single-pixel occupies 0.16 mm2 and consumes 12 µW (recording part) and 22 µW (stimulation blocks).

Change in brain electrical activity connectivity in migraine patients without aura

Objective: In this study, we consider how the structure of the brain EEG activity changes in patients with migraine, compared with virtually healthy volunteers without complaints of acute or chronic headache. Materials and Methods. The study of the connectivity of EEG activity was carried out on the basis of an objective assessment of pairwise synchronization between different recording channels, for which we used a method based on wavelet bicoherence. Results. Within the framework of the performed experimental study, we demonstrated an increased reactivity in the structure of connections in brain electrical activity of the patients experiencing a weak visual impact. Conclusion. A prospective study could determine the value of the described diagnostic procedure in support of the clinical decision on appropriate pharmacological and non-pharmacological prophylactic measures.

Vernier time-to-digital converter with ring oscillators for in-pixel time-of-arrival and time-over-threshold measurement in 28 nm CMOS

The paper describes a design of a prototype chip in 28 nm CMOS technology, consisting of 8 × 4 pixels with 50 μm pitch, dedicated for the precise measurement of Time-of-Arrival (ToA) and Time-over-Threshold (ToT) with a resolution within the picosecond range. To address this requirement, in-pixel Vernier time-to-digital converter (TDC) has been implemented, which utilizes two ring oscillators per pixel. Overall chip architecture is introduced as well as pixel architecture and selected simulation results. The pixel consists of a recording channel and TDC part. The recording channel is composed of an inverter-based front-end amplifier with Zimmerman feedback, a discriminator, a calibration block and a threshold setting block. TDC part includes two ring oscillators together with their calibration blocks and additional logic with counters/shift registers that allow for precise ToA measurement (using Vernier method) as well as ToT measurement (using one of the oscillators). Alternatively, single photon counting (SPC) mode can be used. Frequency of oscillators is set in three steps. First, two global 8-bit digital-to-analog converters (DACs) are used for initial setting of all ring oscillators. Then, per-oscillator capacitance bank and 6-bit DAC are used for fine setting. Simulation results of core blocks suggest that the ToA resolution on the order of tens of picoseconds may be achieved. The chips are already fabricated and are currently being prepared for measurements.

Decoding of cortex-wide brain activity from local recordings of neural potentials

Objective. Electrical recordings of neural activity from brain surface have been widely employed in basic neuroscience research and clinical practice for investigations of neural circuit functions, brain–computer interfaces, and treatments for neurological disorders. Traditionally, these surface potentials have been believed to mainly reflect local neural activity. It is not known how informative the locally recorded surface potentials are for the neural activities across multiple cortical regions. Approach. To investigate that, we perform simultaneous local electrical recording and wide-field calcium imaging in awake head-fixed mice. Using a recurrent neural network model, we try to decode the calcium fluorescence activity of multiple cortical regions from local electrical recordings. Main results. The mean activity of different cortical regions could be decoded from locally recorded surface potentials. Also, each frequency band of surface potentials differentially encodes activities from multiple cortical regions so that including all the frequency bands in the decoding model gives the highest decoding performance. Despite the close spacing between recording channels, surface potentials from different channels provide complementary information about the large-scale cortical activity and the decoding performance continues to improve as more channels are included. Finally, we demonstrate the successful decoding of whole dorsal cortex activity at pixel-level using locally recorded surface potentials. Significance. These results show that the locally recorded surface potentials indeed contain rich information of the large-scale neural activities, which could be further demixed to recover the neural activity across individual cortical regions. In the future, our cross-modality inference approach could be adapted to virtually reconstruct cortex-wide brain activity, greatly expanding the spatial reach of surface electrical recordings without increasing invasiveness. Furthermore, it could be used to facilitate imaging neural activity across the whole cortex in freely moving animals, without requirement of head-fixed microscopy configurations.

Unsupervised Channel Compression Methods in Motor Prostheses Design.

The development of high performance brain machine interfaces (BMIs) requires scaling recording channel count to enable simultaneous recording from large populations of neurons. Unfortunately, proposed implantable neural interfaces have power requirements that scale linearly with channel count. To facilitate the design of interfaces with reduced power requirements, we propose and evaluate an unsupervised-learning-based compressed sensing strategy. This strategy suggests novel neural interface architectures which compress neural data by methodically combining channels of spiking activity. We develop an entropy-based compression strategy that models the population of neurons as being generated from a lower dimensional set of latent variables and aims to minimize the loss of information in the latent variables due to compression. We evaluate compressed features by inferring the latent variables from these features and measuring the accuracy with which the activity of held out neurons and arm movements can be estimated. We apply these methods to different cortical regions (PMd and M1) and compare the proposed compression methods to a random projections strategy often employed for compressed sensing and to a supervised regression based channel dropping strategy traditionally applied in BMI applications.

The impact of reducing signal acquisition specifications on neuronal spike sorting.

Measuring electrical potentials in the extracellular space of the brain is a popular technique because it can detect action potentials from putative individual neurons. Electrophysiology is undergoing a transformation where the number of recording channels, and thus number of neurons detected, is growing at a dramatic rate. This rapid scaling is paving the way for both new discoveries and commercial applications; however, as the number of channels increases there will be an increasing need to make these systems more power efficient. One area ripe for optimization are the signal acquisition specifications needed to detect and sort action potentials (i.e., "spikes") to putative single neuron sources. In this work, we take existing recordings collected using Intan hardware and modify them in a way that corresponds to reduced recording performance. The accuracy of these degraded recordings to spike sort using MountainSort4 is evaluated by comparing against expert labels. We show that despite reducing signal specifications by a factor of 2 or more, spike sorting accuracy does not change substantially. Specifically, reducing both sample rate and bit depth from 30 kHz and 16 bits to 12 kHz and 12 bits resulted in a 3% drop in spike sorting accuracy. Our results suggest that current neural acquisition systems are over-specified. These results may inform the design of next generation neural acquisition systems enabling higher channel count systems.

A markerless platform for ambulatory systems neuroscience.

Motor systems neuroscience seeks to understand how the brain controls movement. To minimize confounding variables, large-animal studies typically constrain body movement from areas not under observation, ensuring consistent, repeatable behaviors. Such studies have fueled decades of research, but they may be artificially limiting the richness of neural data observed, preventing generalization to more natural movements and settings. Neuroscience studies of unconstrained movement would capture a greater range of behavior and a more complete view of neuronal activity, but instrumenting an experimental rig suitable for large animals presents substantial engineering challenges. Here, we present a markerless, full-body motion tracking and synchronized wireless neural electrophysiology platform for large, ambulatory animals. Composed of four depth (RGB-D) cameras that provide a 360° view of a 4.5-square-meters enclosed area, this system is designed to record a diverse range of neuroethologically relevant behaviors. This platform also allows for the simultaneous acquisition of hundreds of wireless neural recording channels in multiple brain regions. As behavioral and neuronal data are generated at rates below 200 megabytes per second, a single desktop can facilitate hours of continuous recording. This setup is designed for systems neuroscience and neuroengineering research, where synchronized kinematic behavior and neural data are the foundation for investigation. By enabling the study of previously unexplored movement tasks, this system can generate insights into the functioning of the mammalian motor system and provide a platform to develop brain-machine interfaces for unconstrained applications.

Visualizing spatial differences in corneal electroretinogram potentials using a three-dimensional surface spline

Objective. The spatial distribution of activity at the retina determines the spatial distribution of electroretinogram potentials at the cornea. Here a three-dimensional surface spline method is evaluated for interpolating corneal potentials between measurement points in multi-electrode electroretinography (meERG) data sets. Approach. 25-channel meERG responses were obtained from rat eyes before and after treatment to create local lesions. A 3rd order surface spline was used to interpolate meERG values resulting in smooth color-coded maps of corneal potentials. Potential maps were normalized using standard score values. Pre- and post-treatment responses were characterized by spatial standard deviation and by difference-from-normal plots. Main results. The spatial standard deviation for eyes with local lesions were significantly higher than for healthy eyes. The 3rd order spline resulted in well-behaved corneal potential maps that maintained low error rate when up to 30% of recording channels were excluded from analysis. Post-normalization, responses could be combined within experimental groups, and individual eyes with lesions were clearly distinguished from the healthy-eye mean response. A 3rd order surface spline is an acceptable means of interpolating meERG potentials to create corneal potential maps. The spatial standard deviation is more sensitive to local dysfunction than absolute amplitudes. Significance. This work demonstrates solutions to key challenges in the recording and analysis of meERG responses: visualization, normalization, channel loss, and identification of abnormal responses. Continued development of the meERG technique is relevant to research and clinical applications, especially where local dysfunction (early progressive disease) or local therapeutic effect (subretinal injection) is of interest.

Automatic decomposition of pediatric high density surface EMG: A pilot study

This pilot study presents a novel application of high density surface electromyography (EMG) decomposition in pediatric patients, with a view toward promoting its potential clinical application for examination of pediatric neuromuscular diseases. Automatic progressive FastICA peel-off (APFP) framework was used to decompose high density surface EMG signals recorded from the first dorsal interosseous and abductor pollicis brevis muscles of children with spinal muscular atrophy. The performance of the decomposition with reduced EMG recording channels was also investigated. In total 131 motor units were extracted from 15 trials of 64-channel (8 ​× ​8) surface EMG. The decomposition yield reduced to 102 motor units when 36-channel (6 ​× ​6) was used, among which 84 motor units were common ones with an average matching rate of (95.68 ​± ​5.26) %. The decomposition yield further reduced to 65 motor units when only 16-channel (4 ​× ​4) was used, among which 57 were common ones with an average matching rate of (95.99 ​± ​4.56) %. These results indicate that using the APFP framework single motor unit activity can be reliably and automatically extracted from pediatric surface EMG signals recorded by an electrode array, which can facilitate potential clinical application of surface EMG as an alternative or supplement to invasive needle EMG for examination of neuromuscular diseases in children.

Compressed Sensing of Extracellular Neurophysiology Signals: A Review.

This article presents a comprehensive survey of literature on the compressed sensing (CS) of neurophysiology signals. CS is a promising technique to achieve high-fidelity, low-rate, and hardware-efficient neural signal compression tasks for wireless streaming of massively parallel neural recording channels in next-generation neural interface technologies. The main objective is to provide a timely retrospective on applying the CS theory to the extracellular brain signals in the past decade. We will present a comprehensive review on the CS-based neural recording system architecture, the CS encoder hardware exploration and implementation, the sparse representation of neural signals, and the signal reconstruction algorithms. Deep learning-based CS methods are also discussed and compared with the traditional CS-based approaches. We will also extend our discussion to cover the technical challenges and prospects in this emerging field.

Versatile Surface Electrodes for Combined Electrophysiology and Two-Photon Imaging of the Mouse Central Nervous System.

Understanding and modulating CNS function in physiological as well as pathophysiological contexts remains a significant ambition in research and clinical applications. The investigation of the multifaceted CNS cell types including their interactions and contributions to neural function requires a combination of the state-of-the-art in vivo electrophysiology and imaging techniques. We developed a novel type of liquid crystal polymer (LCP) surface micro-electrode manufactured in three customized designs with up to 16 channels for recording and stimulation of brain activity. All designs include spare central spaces for simultaneous 2P-imaging. Nanoporous platinum-plated contact sites ensure a low impedance and high current transfer. The epidural implantation of the LCP micro-electrodes could be combined with standard cranial window surgery. The epidurally positioned electrodes did not only display long-term biocompatibility, but we also observed an additional stabilization of the underlying CNS tissue. We demonstrate the electrode’s versatility in combination with in vivo 2P-imaging by monitoring anesthesia-awake cycles of transgenic mice with GCaMP3 expression in neurons or astrocytes. Cortical stimulation and simultaneous 2P Ca2+ imaging in neurons or astrocytes highlighted the astrocytes’ integrative character in neuronal activity processing. Furthermore, we confirmed that spontaneous astroglial Ca2+ signals are dampened under anesthesia, while evoked signals in neurons and astrocytes showed stronger dependency on stimulation intensity rather than on various levels of anesthesia. Finally, we show that the electrodes provide recordings of the electrocorticogram (ECoG) with a high signal-to noise ratio and spatial signal differences which help to decipher brain activity states during experimental procedures. Summarizing, the novel LCP surface micro-electrode is a versatile, convenient, and reliable tool to investigate brain function in vivo.

A 400 mV 160 nW/Ch Compact Energy Efficient ∆Σ Modulator for Multichannel Biopotential Signal Acquisition System.

Analog to digitalconverter circuit design for biomedical systems with multiple recording channels presents challenges in high density and very low power consumption. Passive integrator and loop-filter based delta-sigma modulators (DSMs) have been recently reported for ultra-low-power and highly energy-efficient data converters for multi-channel biopotential acquisition. However, these modulators rely on a very high oversampling ratio (OSR) to achieve the target resolution. Higher OSR leads to higher power consumption in the modulator and the digital low-pass and decimation filter succeeding the DSM. We present a low OSR passive integrator-based DSM in this work by relying on a duty-cycled resistor (DCR). DCR enables the realization of large time constants in the already passive loop-filter, with minimal area and overhead power consumption. This leads to design of DSMs that are highly area, power and energy-efficient, suitable for multi-channel biopotential recording systems. We demonstrate a second order, duty-cycled passive integrator based CTDSM in a 65 nm CMOS technology for a 10 kHz biopotential bandwidth. Measurement results show that the fabricated design achieves an SNDR/DR of 56.36/63.1 dB while consuming only 160 nW power with an OSR of 32 and occupies an area of 0.035 mm 2 with a state-of-the-art energy efficiency of 14.9 fJ/conv. In-vitro and in-vivo measurements are provided to further demonstrate the operation of the proposed DSM.

P-OT015. Monitoring prefrontal cortical activation while driving a vehicle: A functional near-infrared spectroscopy study

Introduction . Functional near-infrared spectroscopy (fNIRS) is a non-invasive method to measure changes in oxygenated- and deoxygenated-hemoglobin concentrations in response to neural activation during specific tasks and stimuli. In this study, we monitored the brain activity of the prefrontal cortex in subjects while driving to investigate the effects of speed on brain activity. Methods . Twenty healthy subjects with driving experience drove their own vehicles about 100 meters at any speed (5 trials) and at highest acceleration speed (5 trials) and were told to stop at the stop line. Sixteen channels of fNIRS recording were placed to overlie the prefrontal cortex of the subjects during driving. The distance between the stop line and the vehicle when the vehicle comes to a stop and oxygenated hemoglobin concentration was compared between driving at any speed and driving at the highest acceleration. Results . Variation in the distance between the stop line and the vehicle was significantly greater when driving at the highest acceleration compared to driving at any speed. Oxygenated hemoglobin concentration recorded in the prefrontal cortex was significantly greater while driving a car to a stop at the highest acceleration than at free speed. Conclusion . Our findings suggest that driving at highest acceleration speeds requires more attention resources than driving at any speed. We have shown that monitoring brain activity using fNIRS has advantages and usefulness of in real driving.

Clinical cases of difficulty in interpreting the results of 24 hour blood pressure monitoring in men of military age

The article describes 2 clinical cases with signs of deliberate manipulation by patients of the results of ambulatory BP monitoring, leading to an increase of mean blood pressure. Such findings, as experience shows, are often revealed in men of military age. The value of additional recording channels, such as ECG, physical activity and body position, rheopneumogram, for an objective assessment of such findings is shown. The main factors that can lead to an increase in blood pressure during ABPM are listed. Conclusion examples for describing such changes are given.

Full-Band EEG Recordings Using Hybrid AC/DC-Divider Filters.

Full-band DC recordings enable recording of slow electrical brain signals that are severely compromised during conventional AC recordings. However, full-band DC recordings may be limited by the amplifier's dynamic input range and the loss of small amplitude high-frequency signals. Recently, Neuralynx has proposed full-band recordings with inverse filtering for signal reconstruction based on hybrid AC/DC-divider RRC filters that enable only partial suppression of DC signals. However, the quality of signal reconstruction for biological signals has not yet been assessed. Here, we propose a novel digital inverse filter based on a mathematical model describing RRC filter properties, which provides high computational accuracy and versatility. Second, we propose procedures for the evaluation of the inverse filter coefficients, adapted for each recording channel to minimize the error caused by the deviation of the real values of the RRC filter elements from their nominal values. We demonstrate that this approach enables near 99% reconstruction quality of high-potassium-induced cortical spreading depolarizations (SDs), endothelin-induced ischemic negative ultraslow potentials (NUPs), and whole-cell recordings of membrane potential using RRC filters. The quality of the reconstruction was significantly higher than with the existing inverse filtering procedures. Thus, RRC filters with inverse filtering are optimal for full-band EEG recordings in various applications.

Optimal Neuro-Fuzzy Equalizers for Detecting Nonlinear Distortion Channels of the Perpendicular Magnetic Recording System

Nonlinear distortions caused by partial erasure and nonlinear transition shifts interacting with inter-symbol interference, are a major hindrance to data storage systems, since they degrade detector performance. This work aims to design and optimize the neuro-fuzzy equalizer (NFE) using the multi-objective genetic algorithm (MOGA) to detect nonlinear high-density magnetic recording (MR) channels. Through the GA-assisted back-propagation algorithm and least mean square optimization, the complexity in terms of decision rules is reduced by 25% and significantly provides 65% lower signal processing computation. When applied to the perpendicular (MR) system, the proposed NFE outperforms existing equalizers such as the neural network-based equalizer, fuzzy logic equalizer, and conventional NFE for the Volterra and jitter media noise channels using 1–3 dB and 1.5–3.5 dB signal-to-noise ratio gains at the bit-error-rate of 10-4, respectively. Furthermore, compared to the other models, the NFE provides a more effective output mean square error performance for retrieving the original bit data.

Duplicate Detection of Spike Events: A Relevant Problem in Human Single-Unit Recordings.

Single-unit recordings in the brain of behaving human subjects provide a unique opportunity to advance our understanding of neural mechanisms of cognition. These recordings are exclusively performed in medical centers during diagnostic or therapeutic procedures. The presence of medical instruments along with other aspects of the hospital environment limit the control of electrical noise compared to animal laboratory environments. Here, we highlight the problem of an increased occurrence of simultaneous spike events on different recording channels in human single-unit recordings. Most of these simultaneous events were detected in clusters previously labeled as artifacts and showed similar waveforms. These events may result from common external noise sources or from different micro-electrodes recording activity from the same neuron. To address the problem of duplicate recorded events, we introduce an open-source algorithm to identify these artificial spike events based on their synchronicity and waveform similarity. Applying our method to a comprehensive dataset of human single-unit recordings, we demonstrate that our algorithm can substantially increase the data quality of these recordings. Given our findings, we argue that future studies of single-unit activity recorded under noisy conditions should employ algorithms of this kind to improve data quality.