The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems.

: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems.

Edge computing on TPU for brain implant signal analysis.

The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision offline evaluation and separate solutions for embedded systems where computational resources are more limited. We propose a deep learning-based spike sorting system, that utilizes both unsupervised and supervised paradigms to learn a general feature embedding space and detect neural activity in raw data as well as predict the feature vectors for sorting.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings.

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis.

PlatypOUs-A Mobile Robot Platform and Demonstration Tool Supporting STEM Education.

Given the rising popularity of robotics, student-driven robot development projects are playing a key role in attracting more people towards engineering and science studies. This article presents the early development process of an open-source mobile robot platform-named PlatypOUs-which can be remotely controlled via an electromyography (EMG) appliance using the MindRove brain-computer interface (BCI) headset as a sensor for the purpose of signal acquisition. The gathered bio-signals are classified by a Support Vector Machine (SVM) whose results are translated into motion commands for the mobile platform.

ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution.

The growing number of recording sites of silicon-based probes means that an increasing amount of neural cell activities can be recorded simultaneously, facilitating the investigation of underlying complex neural dynamics. In order to overcome the challenges generated by the increasing number of channels, highly automated signal processing tools are needed. Our goal was to build a spike sorting model that can perform as well as offline solutions while maintaining high efficiency, enabling high-performance online sorting.

The neural tissue around SU-8 implants: A quantitative in vivo biocompatibility study.

The use of SU-8 material in the production of neural sensors has grown recently. Despite its widespread application, a detailed systematic quantitative analysis concerning its biocompatibility in the central nervous system is lacking. In this immunohistochemical study, we quantified the neuronal preservation and the severity of astrogliosis around SU-8 devices implanted in the neocortex of rats, after a 2 months survival.

Dynamic-informed consent: A potential solution for ethical dilemmas in population sequencing initiatives.

While the majority of population-level genome sequencing initiatives claim to follow the principles of informed consent, the requirements for informed consent have not been-well defined in this context. In fact, the implementation of informed consent differs greatly across these initiatives - spanning broad consent, blanket consent, and tiered consent among others. As such, this calls for an investigation into the requirements for consent to be "informed" in the context of population genomics.

Correction: Method for spike detection from microelectrode array recordings contaminated by artifacts of simultaneous two-photon imaging.

[This corrects the article DOI: 10.1371/journal.pone.

Spike detection and sorting with deep learning.

Objective: The extraction and identification of single-unit activities in intracortically recorded electric signals have a key role in basic neuroscience, but also in applied fields, like in the development of high-accuracy brain-computer interfaces. The purpose of this paper is to present our current results on the detection, classification and prediction of neural activities based on multichannel action potential recordings.

Approach: Throughout our investigations, a deep learning approach utilizing convolutional neural networks and a combination of recurrent and convolutional neural networks was applied, with the latter used in case of spike detection and the former used for cases of sorting and predicting spiking activities.

Method for spike detection from microelectrode array recordings contaminated by artifacts of simultaneous two-photon imaging.

The simultaneous utilization of electrophysiological recordings and two-photon imaging allows the observation of neural activity in a high temporal and spatial resolution at the same time. The three dimensional monitoring of morphological features near the microelectrode array makes the observation more precise and complex. In vitro experiments were performed on mice neocortical slices expressing the GCaMP6 genetically encoded calcium indicator for monitoring the neural activity with two-photon microscopy around the implanted microelectrodes.

Informed Consent in Biomedical Research.

Informed consent is the result of tumultuous events in both the clinical and research arenas over the last 100 years. Throughout this time, the notion of informed consent has shifted tremendously, both due to advances in medicine, as well as the type of data being gathered. As such, informed consent has misaligned with the goals of medical research.

Slow insertion of silicon probes improves the quality of acute neuronal recordings.

Neural probes designed for extracellular recording of brain electrical activity are traditionally implanted with an insertion speed between 1 µm/s and 1 mm/s into the brain tissue. Although the physical effects of insertion speed on the tissue are well studied, there is a lack of research investigating how the quality of the acquired electrophysiological signal depends on the speed of probe insertion. In this study, we used four different insertion speeds (0.

Towards an efficient and Energy-Aware mobile big health data architecture.

Background And Objectives: Mobile and ubiquitous devices are everywhere, generating an exorbitant amount of data. New generations of healthcare systems are using mobile devices to continuously collect large amounts of different types of data from patients with chronic diseases. The challenge with such Mobile Big Data in general, is how to meet the growing performance demands of the mobile resources handling these tasks, while simultaneously minimizing their consumption.

Neurobiochemical changes in the vicinity of a nanostructured neural implant.

Neural interface technologies including recording and stimulation electrodes are currently in the early phase of clinical trials aiming to help patients with spinal cord injuries, degenerative disorders, strokes interrupting descending motor pathways, or limb amputations. Their lifetime is of key importance; however, it is limited by the foreign body response of the tissue causing the loss of neurons and a reactive astrogliosis around the implant surface. Improving the biocompatibility of implant surfaces, especially promoting neuronal attachment and regeneration is therefore essential.

A Multimodal, SU-8 - Platinum - Polyimide Microelectrode Array for Chronic In Vivo Neurophysiology.

Utilization of polymers as insulator and bulk materials of microelectrode arrays (MEAs) makes the realization of flexible, biocompatible sensors possible, which are suitable for various neurophysiological experiments such as in vivo detection of local field potential changes on the surface of the neocortex or unit activities within the brain tissue. In this paper the microfabrication of a novel, all-flexible, polymer-based MEA is presented. The device consists of a three dimensional sensor configuration with an implantable depth electrode array and brain surface electrodes, allowing the recording of electrocorticographic (ECoG) signals with laminar ones, simultaneously.

Durability of high surface area platinum deposits on microelectrode arrays for acute neural recordings.

The durability of high surface area platinum electrodes during acute intracerebral measurements was investigated. Electrode sites with extremely rough surfaces were realized using electrochemical deposition of platinum onto silicon-based microelectrode arrays from a lead-free platinizing solution. The close to 1000-fold increase in effective surface area lowered impedance, its absolute value at 1 kHz became about 7 and 18 % of the original Pt electrodes in vitro and in vivo, respectively.