BACKGROUND: Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. OBJECTIVES: Investigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. METHODS: Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination. RESULTS: The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. CONCLUSIONS: Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration.

• MeSH
• Algorithms MeSH
• Artifacts MeSH
• Electrocardiography MeSH
• Essential Tremor * MeSH
• Humans MeSH
• Brain-Computer Interfaces * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Neuronal Plasticity MeSH
• Trauma, Nervous System rehabilitation   MeSH
• Nerve Regeneration MeSH
• Rehabilitation MeSH
• Publication type
• Monograph MeSH

• Conspectus
• Patologie. Klinická medicína
• NML Fields
• neurologie
• traumatologie
• neurochirurgie

Objective.Functional specialization is fundamental to neural information processing. Here, we study whether and how functional specialization emerges in artificial deep convolutional neural networks (CNNs) during a brain-computer interfacing (BCI) task.Approach.We trained CNNs to predict hand movement speed from intracranial electroencephalography (iEEG) and delineated how units across the different CNN hidden layers learned to represent the iEEG signal.Main results.We show that distinct, functionally interpretable neural populations emerged as a result of the training process. While some units became sensitive to either iEEG amplitude or phase, others showed bimodal behavior with significant sensitivity to both features. Pruning of highly sensitive units resulted in a steep drop of decoding accuracy not observed for pruning of less sensitive units, highlighting the functional relevance of the amplitude- and phase-specialized populations.Significance.We anticipate that emergent functional specialization as uncovered here will become a key concept in research towards interpretable deep learning for neuroscience and BCI applications.

• MeSH
• Algorithms MeSH
• Electroencephalography methods   MeSH
• Brain MeSH
• Neural Networks, Computer MeSH
• Brain-Computer Interfaces * MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

This paper describes an ongoing project that has the aim to develop a low cost application to replace a computer mouse for people with physical impairment. The application is based on an eye tracking algorithm and assumes that the camera and the head position are fixed. Color tracking and template matching methods are used for pupil detection. Calibration is provided by neural networks as well as by parametric interpolation methods. Neural networks use back-propagation for learning and bipolar sigmoid function is chosen as the activation function. The user’s eye is scanned with a simple web camera with backlight compensation which is attached to a head fixation device. Neural networks significantly outperform parametric interpolation techniques: 1) the calibration procedure is faster as they require less calibration marks and 2) cursor control is more precise. The system in its current stage of development is able to distinguish regions at least on the level of desktop icons. The main limitation of the proposed method is the lack of head-pose invariance and its relative sensitivity to illumination (especially to incidental pupil reflections).

• MeSH
• Photography methods   MeSH
• Image Interpretation, Computer-Assisted methods   MeSH
• Humans MeSH
• Young Adult MeSH
• Neural Networks, Computer MeSH
• Eye Movements physiology   MeSH
• Retina anatomy & histology   physiology   MeSH
• Retinoscopy methods   MeSH
• Pattern Recognition, Automated methods   MeSH
• Sensitivity and Specificity MeSH
• User-Computer Interface MeSH
• Check Tag
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH

Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.

• MeSH
• Algorithms * MeSH
• Artifacts MeSH
• Electroencephalography * methods   MeSH
• Humans MeSH
• Brain * physiology   MeSH
• Brain-Computer Interfaces MeSH
• Machine Learning MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

Brain-computer interface (BCI) provides direct communication between the brain and an external device. BCI systems have become a trendy field of research in recent years. These systems can be used in a variety of applications to help both disabled and healthy people. Concerning significant BCI progress, we may assume that these systems are not very far from real-world applications. This review has taken into account current trends in BCI research. In this survey, 100 most cited articles from the WOS database were selected over the last 4 years. This survey is divided into several sectors. These sectors are Medicine, Communication and Control, Entertainment, and Other BCI applications. The application area, recording method, signal acquisition types, and countries of origin have been identified in each article. This survey provides an overview of the BCI articles published from 2016 to 2020 and their current trends and advances in different application areas.

• MeSH
• Electroencephalography MeSH
• Humans MeSH
• Brain MeSH
• Communication Devices for People with Disabilities * MeSH
• Brain-Computer Interfaces * MeSH
• User-Computer Interface MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

Objective.Understanding how the retina converts a natural image or an electrically stimulated one into neural firing patterns is the focus of on-going research activities.Ex vivo, the retina can be readily investigated using multi electrode arrays (MEAs). However, MEA recording and stimulation from an intact retina (in the eye) has been so far insufficient.Approach.In the present study, we report new soft carbon electrode arrays suitable for recording and stimulating neural activity in an intact retina. Screen-printing of carbon ink on 20μm polyurethane (PU) film was used to realize electrode arrays with electrodes as small as 40μm in diameter. Passivation was achieved with a holey membrane, realized using laser drilling in a thin (50μm) PU film. Plasma polymerized 3.4-ethylenedioxythiophene was used to coat the electrode array to improve the electrode specific capacitance. Chick retinas, embryonic stage day 13, both explanted and intact inside an enucleated eye, were used.Main results.A novel fabrication process based on printed carbon electrodes was developed and yielded high capacitance electrodes on a soft substrate.Ex vivoelectrical recording of retina activity with carbon electrodes is demonstrated. With the addition of organic photo-capacitors, simultaneous photo-electrical stimulation and electrical recording was achieved. Finally, electrical activity recordings from an intact chick retina (inside enucleated eyes) were demonstrated. Both photosensitive retinal ganglion cell responses and spontaneous retina waves were recorded and their features analyzed.Significance.Results of this study demonstrated soft electrode arrays with unique properties, suitable for simultaneous recording and photo-electrical stimulation of the retina at high fidelity. This novel electrode technology opens up new frontiers in the study of neural tissuein vivo.

• MeSH
• Electric Stimulation methods   MeSH
• Microelectrodes MeSH
• Retina * physiology   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

BACKGROUND: The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field. RESULTS: Here we present ENNGene-Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein. CONCLUSIONS: As the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.

• MeSH
• Genomics MeSH
• Neural Networks, Computer * MeSH
• Protein Structure, Secondary MeSH
• Machine Learning * MeSH
• Publication type
• Journal Article MeSH

The goal of every human being on our planet is to improve the living conditions not only of his life, but also of all humanity. Digitization, dynamic development of technological equipment, unique software solutions and the transfer of human capabilities into the form of data enable the gradual achievement of this goal. The human brain is the source of all activities (physical, mental, decision-making, etc.) that a person performs. Therefore, the main goal of research is its functioning and the possibility to at least partially replace this functioning by external devices connected to a computer. The Brain-Computer Interface (BCI) is a term which represents a tool for performing external activities through sensed signals from the brain. This document describes various techniques that can be used to collect the neural signals. The measurement can be invasive or non-invasive. Electroencephalography (EEG) is the most studied non-invasive method and is therefore described in more detail in the presented paper. Once the signals from the brain are scanned, they need to be analysed in order to interpret them as computer commands. The presented methods of EEG signal analysis have advantages and disadvantages, either temporal or spatial. The use of the inverse EEG problem can be considered as a new trend to solve non-invasive high-resolution BCI.

• MeSH
• Spectroscopy, Near-Infrared methods   MeSH
• Diagnostic Techniques, Neurological MeSH
• Electroencephalography methods   instrumentation   MeSH
• Electrooculography methods   MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Magnetoencephalography methods   MeSH
• Neuroimaging MeSH
• Positron-Emission Tomography MeSH
• Brain-Computer Interfaces * MeSH
• Evoked Potentials, Visual MeSH
• Check Tag
• Humans MeSH

Boron-doped nanocrystalline diamond (BDD) electrodes have recently attracted attention as materials for neural electrodes due to their superior physical and electrochemical properties, however their biocompatibility remains largely unexplored. In this work, we aim to investigate the in vivo biocompatibility of BDD electrodes in relation to conventional titanium nitride (TiN) electrodes using a rat subcutaneous implantation model. High quality BDD films were synthesized on electrodes intended for use as an implantable neurostimulation device. After implantation for 2 and 4 weeks, tissue sections adjacent to the electrodes were obtained for histological analysis. Both types of implants were contained in a thin fibrous encapsulation layer, the thickness of which decreased with time. Although the level of neovascularization around the implants was similar, BDD electrodes elicited significantly thinner fibrous capsules and a milder inflammatory reaction at both time points. These results suggest that BDD films may constitute an appropriate material to support stable performance of implantable neural electrodes over time.

• Publication type
• Journal Article MeSH