Recorded Brain Signals Research Articles

Semantic category-based decoding of human brain activity using a Gabor-based model by estimating intracranial field potential range in temporal cortex.

Predicting and decoding the recorded neural activity for visual stimuli is the topic of many studies. This prediction can be made by comparing the model's responses to different stimuli with the available recorded brain signal. The neural activities can be decoded then by finding the stimulus which has generated the nearest model's response to the recorded signal. In this study, a model is proposed which can estimate the response of human brain to images from different conceptual categories by inserting the visual stimuli as the model input after filtering by Gabor wavelets. This helped us to find each image's low level visual features. Afterward, the extracted image features were applied to the input of a curve fitting neural network. As the output, the range of intracranial field potential was estimated. This was performed separately for each pixel of the image. To evaluate the model's accuracy, two factors were used, namely the Pearson correlation and Normalized root mean square error. The results show that the proposed model can accurately estimate the brain's response to conceptual categories To decode the brain' activity based on the observed semantic category in each test observations by using of the model, we calculated the distance between the recorded signal and the model responses to all stimuli from different categories and assigned the category of the nearest model response to brain's response in that trial. To this end, a K-nearest neighbors classifier based on Euclidean distance was used. This leaded to a classification accuracy which was significantly higher than chance level. So, the proposed model can be used to decode the activity of the brain in response to the visual stimuli.

Fuzzy Entropy Analysis of the Electroencephalogram in Patients with Alzheimer's Disease: Is the Method Superior to Sample Entropy?

Alzheimer’s disease (AD) is the most prevalent form of dementia in the world, which is characterised by the loss of neurones and the build-up of plaques in the brain, causing progressive symptoms of memory loss and confusion. Although definite diagnosis is only possible by necropsy, differential diagnosis with other types of dementia is still needed. An electroencephalogram (EEG) is a cheap, portable, non-invasive method to record brain signals. Previous studies with non-linear signal processing methods have shown changes in the EEG due to AD, which is characterised reduced complexity and increased regularity. EEGs from 11 AD patients and 11 age-matched control subjects were analysed with Fuzzy Entropy (FuzzyEn), a non-linear method that was introduced as an improvement over the frequently used Approximate Entropy (ApEn) and Sample Entropy (SampEn) algorithms. AD patients had significantly lower FuzzyEn values than control subjects (p < 0.01) at electrodes T6, P3, P4, O1, and O2. Furthermore, when diagnostic accuracy was calculated using Receiver Operating Characteristic (ROC) curves, FuzzyEn outperformed both ApEn and SampEn, reaching a maximum accuracy of 86.36%. These results suggest that FuzzyEn could increase the insight into brain dysfunction in AD, providing potentially useful diagnostic information. However, results depend heavily on the input parameters that are used to compute FuzzyEn.

REMOVAL OF ELECTROENCEPHALOGRAPHY EEG EYE-BLINK ARTIFACT USING WAVELET FUNCTION

This paper presents a study related to awareness about concepts of Electroencephalography (EEG) and it's one type of artifact i.e. Eye-Blink Artifact. This is one form of Ocular Artifact. To study brain functioning and problems if any, Neurologists uses Electroencephalography technique. But while recording brain signals, noise (Artifact) gets induced and became part of EEG recording. This creates problems for Nero Experts to analyse and interpret it and hence it is necessary to remove such artifact from recordings. Ocular artifact related to Eye movement is in existence which may also resemble to disordered pattern. One can detect and remove this using Wavelet Function available.Sub sampling and multi resolution analysis of signal is the key idea for identifying typical trendy pattern of required frequency band and nullifying their impact. Discrete Wavelet Transform provides sufficient information both for analysis and synthesis of the original signal.MODWT, IMODWT and MODWTMRA are the wavelet functions used to localise required pattern and remove from the signal.

The Introduction of Designing a Hybrid Brain Computer Interface System

Background: Brain Computer Interface (BCI) is a communication system between human brain and a computer or a peripheral device which by recording brain signals directly would send messages and commands from the human brain to computer. According to brain activity patterns of EEG, BCIs are divided into different types. The most important of these patterns called ERP (Event Related Potentials) which appears after particular events in the EEG signal. Combining various types of BCI systems is called hybrid BCI and increases the efficiency of BCI system. Materials and Methods: In this project, our concentration is on a hybrid P300-SSVEP Speller BCI system in order to enhance the accuracy. The aim of this project is to design a BCI system based on P300 and SSVEP patterns, which sequentially can be able to rectify the weakness points of the conventional BCIs. Results: Our hybrid BCI system consists of two sections including SSVEP condition and P300 condition. First, we have only six frequencies in a 6×6 speller matrix that we should detect one-group characters with same frequencies through six groups. Second, we must detect desired character that there is in selected group in previous section through 36 characters. We used two different types of software to design our hybrid BCI and we achieved a relatively high classification accuracy with our hybrid system. In order to develop a practical daily use EEG system, signals were captured with a standard low cost EMOTIV-Epoc system. Conclusion: we designed a hybrid SSVEP-P300 BCI platform by two different software. Our hybrid BCI system consists of two sections including SSVEP condition and P300 condition. In first section, as respect we had only six frequencies in a 6×6 speller matrix, we should detect one-group characters with the same frequencies through six groups. In second section, we must detect desired character that there is in selected group in previous section. This hybrid system can help to SSVEP condition to choose more commands using P300 condition so that it can help to increase the number of commands from 6 to 36 commands. In our system, we can achieve only six choices by SSVEP condition.

Study on empathetic-pain perception in brain induced by three levels of empathetic-pain perception stimuli

Empathetic-pain perception is a divergence from empathy which is a pain perceived as a reflection of perception from others. The study of empathetic-pain perception and empathy were always related with psychological disorder effecting social and humanity values. The process involved in empathetic-pain perception formations in brain were believed to be different if induced by different level of empathetic-pain perception stimuli. Therefore, this paper was aimed to study the processes involved in empathetic-pain perception formation by revealing the activation-time intervals and source location of the highest empathetic-pain perception intensity. This study conducted an experiment to induce empathetic-pain perception on 16 participants using still pictures as visual-stimuli. Electroencephalograph (EEG) recorded brain signal of the participants during the visual-stimuli presentations while the EEG signal were analysed using MATLAB® toolbox, EEGLAB. Time/frequency decomposition in EEGLAB produces ERSP images which determines the activation-time intervals for empathetic-pain perception and, by performing source localization within the activation-time intervals using sLORETA, the source locations for most active processes in empathetic-pain perception were determined. The processes involved in empathetic-pain perception formation in every level were ‘stimuli-learning’ and ‘memory-reconstructions’ by Posterior Cingulate BA 30, pain-regulation by either Postcentral Gyrus BA 2, Cingulate Gyrus BA 24 or both, and visual-stimuli and visual-memory processing by Lingual Gyrus at almost similar time intervals. However, the processes were also performed by various brain areas to either perform attention-sustain process while managed working memory and self-control regulation by Middle Frontal Gyrus BA 46, mirror-neurons activation while processed attention information and emotions by Inferior Parietal Lobule BA 40, multisensory integration by Superior Temporal Gyrus BA 22, or motor-neurons activation to control the skeletal system respectively in every level by Paracentral Lobule BA 6 and Precentral Gyrus BA44. In conclusion, the empathetic-pain perception formation process discovery were necessary to differentiate every affectional level of the empathetic-pain perception.

Channel Selection with EEG Source Imaging

Brain computer interface (BCI) is a technology recording brain signals and translate into machine language to drive device movement. To improve the convenience and practicability of BCI systems, it is necessary to reduce the number of channels of EEG without reducing classification accuracy. In this paper, a channel selection method was proposed. Combine continuous wavelet transform (CWT) with EEG source imaging (ESI), we can find out the source distribution of two kinds of motor imagery (MI) tasks on the cortex. Then the different value of source distribution on the cortex was projected to scalp. The value on the scalp was treated as a criterion for channel selection. While ESI was applied for locating the active areas, we can not only find out the prior channels clearly, but also figure out the expected areas briefly. The satisfying results show that the combination of CWT and ESI is a potential method for dealing with MI tasks.

Closed-Loop Neurostimulators: A Survey and A Seizure-Predicting Design Example for Intractable Epilepsy Treatment.

First, existing commercially available open-loop and closed-loop implantable neurostimulators are reviewed and compared in terms of their targeted application, physical size, system-level features, and performance as a medical device. Next, signal processing algorithms as the primary strength point of the closed-loop neurostimulators are reviewed, and various design and implementation requirements and trade-offs are discussed in details along with quantitative examples. The review results in a set of guidelines for algorithm selection and evaluation. Second, the implementation of an inductively-powered seizure-predicting microsystem for monitoring and treatment of intractable epilepsy is presented. The miniaturized system is comprised of two miniboards and a power receiver coil. The first board hosts a 24-channel neurostimulator system on chip fabricated in a [Formula: see text] CMOS technology and performs neural recording, on-chip digital signal processing, and electrical stimulation. The second board communicates recorded brain signals as well as signal processing results wirelessly. The multilayer flexible coil receives inductively-transmitted power. The system is sized at 2 × 2 × 0.7 [Formula: see text] and weighs 6 g. The approach is validated in the control of chronic seizures in vivo in freely moving rats.

TrueNorth-enabled real-time classification of EEG data for brain-computer interfacing.

Brain-computer interfaces are commonly proposed to assist individuals with locked-in syndrome to interact with the world around them. In this paper, we present a pipeline to move from recorded brain signals to real-time classification on a low-power platform, such as IBM's TrueNorth Neurosynaptic System. Our results on a EEG-based hand squeeze task show that using a convolutional neural network and a time preserving signal representation strategy provides a good balance between high accuracy and feasibility in a real-time application. This pathway can be adapted to the management of a variety of conditions, including spinal cord injury, epilepsy and Parkinson's disease.

Improving zero-training brain-computer interfaces by mixing model estimators

Objective. Brain-computer interfaces (BCI) based on event-related potentials (ERP) incorporate a decoder to classify recorded brain signals and subsequently select a control signal that drives a computer application. Standard supervised BCI decoders require a tedious calibration procedure prior to every session. Several unsupervised classification methods have been proposed that tune the decoder during actual use and as such omit this calibration. Each of these methods has its own strengths and weaknesses. Our aim is to improve overall accuracy of ERP-based BCIs without calibration. Approach. We consider two approaches for unsupervised classification of ERP signals. Learning from label proportions (LLP) was recently shown to be guaranteed to converge to a supervised decoder when enough data is available. In contrast, the formerly proposed expectation maximization (EM) based decoding for ERP–BCI does not have this guarantee. However, while this decoder has high variance due to random initialization of its parameters, it obtains a higher accuracy faster than LLP when the initialization is good. We introduce a method to optimally combine these two unsupervised decoding methods, letting one method’s strengths compensate for the weaknesses of the other and vice versa. The new method is compared to the aforementioned methods in a resimulation of an experiment with a visual speller. Main results. Analysis of the experimental results shows that the new method exceeds the performance of the previous unsupervised classification approaches in terms of ERP classification accuracy and symbol selection accuracy during the spelling experiment. Furthermore, the method shows less dependency on random initialization of model parameters and is consequently more reliable. Significance. Improving the accuracy and subsequent reliability of calibrationless BCIs makes these systems more appealing for frequent use.

Feature Extraction Using Independent Component Analysis Method from Non-Invasive Recordings of Electroencephalography (EEG) Brain Signals

Electroencephalography (EEG) is a well known procedure in neuroscience, performed to extract brain signal activity associated with voluntary and involuntary tasks. Scientists and researchers working in neuroscience are involved in the research of brain computer interfacing (BCI) and in improving the existing BCI systems. In BCI, it is possible for a person to control the external devices remotely using brain signals without neurophysical intervention. In the proposed work the new algorithm is introduced to extract the feature from EEG based recorded brain signals. The features are extracted for a specific motoryaction that is raising the right hand. The proposed algorithm is also verified from EEGLAB routines also based on Independent Component Analysis (ICA) method written in MATLAB platform.

EEG processing: a many-core approach utilising the Intel single-chip cloud computer platform

Epilepsy is the most frequent neurological disorder other than stroke. The electroencephalogram (EEG) is the main tool used in monitoring and recording brain signals. In this study, we target two detection algorithms that are essential in the diagnosis of epileptic patients. These algorithms detect high frequency oscillations (HFO) and interictal spikes (IIS) in subdural EEG recordings respectively. This paper presents the efforts on porting both EEG processing algorithms into Intel's concept vehicle, the single-chip cloud computer (SCC), a fully programmable 48-core prototype provided with an on-chip network along with advanced power management technologies and support for message-passing. Several experiments are presented for different SCC configurations, where we vary the number of cores used and their respective voltage/frequency settings. The application was decomposed into two execution regions (i.e., load and execution). Results are presented in the form of performance, power, energy, and energy-delay product (EDP) metrics for each experiment.

EEG processing: a many-core approach utilising the Intel single-chip cloud computer platform

Epilepsy is the most frequent neurological disorder other than stroke. The electroencephalogram (EEG) is the main tool used in monitoring and recording brain signals. In this study, we target two detection algorithms that are essential in the diagnosis of epileptic patients. These algorithms detect high frequency oscillations (HFO) and interictal spikes (IIS) in subdural EEG recordings respectively. This paper presents the efforts on porting both EEG processing algorithms into Intel's concept vehicle, the single-chip cloud computer (SCC), a fully programmable 48-core prototype provided with an on-chip network along with advanced power management technologies and support for message-passing. Several experiments are presented for different SCC configurations, where we vary the number of cores used and their respective voltage/frequency settings. The application was decomposed into two execution regions (i.e., load and execution). Results are presented in the form of performance, power, energy, and energy-delay product (EDP) metrics for each experiment.

ФОРМИРОВАНИЕ НЕЙРОМЕТОДИКИ ПРОФЕССИОНАЛЬНОГО ОБУЧЕНИЯ В УСЛОВИЯХ ЧЕЛОВЕКО-МАШИННОГО ВЗАИМОДЕЙСТВИЯ

The article analyzes the possibilities of bridging the gap between scientific research in neuropedagogy and scientific and technological initiatives in the sphere of creating a neuronet environment in Russia. The initiative is associated with the adoption in 2016 the Neuronet road map within the framework of the National Technological Initiative approved by the Presidium of the Council of the President of the Russian Federation for Modernization of Economy and Innovative Development of Russia. The research is aimed at bridging the gap between the initiatory processes of forming new spheres of neuroscience in human life and the traditional character of adaptation of new ideas in Pedagogy. The authors study the processes of brain activity (neuropaths) in professional activities related to human-machine interactions aimed at developing neuro-methods of vocational training aimed at supporting the formation of sustainable, health-saving modes of human-machine interaction. The subjects are operators of forest machines that use control systems. Professional training of operators in the field of complex human-machine interactions is one of the promising one among the practical applications of neuro-methods for professional education. To study the neuropathy of the professional activity of operators of machines with processor control, the simulators were used, that completely simulated the process of work of the operator in the cabin of a forest machine with processor control. Neuropathy studies were based on recording brain signals using an electroencephalograph by Medikom MTD “Encephalan-EEGR-19/26”. The subjects were both experienced operators and novice operators. The studies of professional actions of operators in the simulation environment in combination with EEG measurements revealed the presence of stable neuropaths having common configuration, both for novice and experienced operators. The results of measurements were presented in two main neuromethodological approaches of neuro-oriented learning and neuro-corrective training. The forming of the neurotechnique of training within the framework of the new technological initiative of Russia will make it possible to obtain methodologically sound scientific and technical products that do not only develop new areas of training in human-machine interaction, but also ensure the health-saving nature of technological innovations.

Elements of person knowledge: Episodic recollection helps us to identify people but not to recognize their faces

Faces automatically draw attention, allowing rapid assessments of personality and likely behaviour. How we respond to people is, however, highly dependent on whether we know who they are. According to face processing models person knowledge comes from an extended neural system that includes structures linked to episodic memory. Here we use scalp recorded brain signals to demonstrate the specific role of episodic memory processes during face processing. In two experiments we recorded Event-Related Potentials (ERPs) while participants made identify, familiar or unknown responses to famous faces. ERPs revealed neural signals previously associated with episodic recollection for identify but not familiar faces. These findings provide novel evidence suggesting that recollection is central to face processing, providing one source of person knowledge that can be used to moderate the initial impressions gleaned from the core neural system that supports face recognition.

Proceedings of the Eighth International Workshop on Advances in Electrocorticography.

Excerpted proceedings of the Eighth International Workshop on Advances in Electrocorticography (ECoG), which convened October 15-16, 2015 in Chicago, IL, are presented. The workshop series has become the foremost gathering to present current basic and clinical research in subdural brain signal recording and analysis.

Neurological Analysis of Consumer Behavior in Shopping Malls Using EEG

Today, shopping centers go beyond purchasing based behavior. As intangible variables, effective social factors also affect consumer behavior. These variables cannot always lead to purchasing but create a potential customer who is exactly exposed to promotional incentives. It should be noted that identifying these intangible factors can only be extracted through non-self-reporting methods. Thus, the use of neuroscience techniques can be a necessity. This study was an exploratory research that applied electroencephalographic tools to record brain signals through EPOC+ 14-Electrode Wireless EEG device. The signals were cleared using EEGLAB Software through ASR Method. The population consisted of two groups of three people (control and experimental group). The seven elements of the shopping centers proposed by Arpita Jare were used to provide stimulus. Thermal map analysis for F3 and F7 electrodes and correlation between the seven elements and brain waves showed that control group had a significant behavior in comparison to experimental group due to their interest in shopping centers (P-Value <.05). Results showed that consumer behavior in shopping centers is not affected by tangible factors such as product brand but social and cultural interactions and intangible elements are also effective that are hidden from marketers and designers’ view.

EEG Analysis for Differentiating between Brain Death and Coma in Humans

To have an accuracy and fast diagnosis of brain death is very urgent for patients especially for distinguishing brain death from coma circumstance. Electroencephalogram is a method of invasive recording brain signals with high temporal resolution. So in clinics, EEG is taken as the most reliable method to estimate the brain death state of affairs. The objective of this paper is to find out the statistical rule on brain death and coma detection through EEG signal processing technology analysis on 20 patients’ medical data (10 for brain death group, 10 for coma group) and then hope to give aid diagnosis conclusions in clinical practice. First, the independent component analysis (ICA) has been applied to solve artifact removal problem combined with wavelet decomposition method to compute the signal noise ratio (SNR). Moreover, the Fourier transform is calculated to compare the power spectrum of the two groups. In addition, we applied the multi-scale permutation entropy into comparison with complexity of brain death and coma patients’ data in different brain inter-rhythms. Finally, the Wilcoxon rank sum has been used to test their statistics significant differences. We proposed our understanding and conclusions on this experiment results. Specially, the real-time brain death and coma patients’ distinguishing is discussed based on parallel computational idea such as cloud computing on Hadoop.

Electrode-electrolyte interface model of tripolar concentric ring electrode and electrode paste.

Electrodes are used to transform ionic currents to electrical currents in biological systems. Modeling the electrode-electrolyte interface could help to optimize the performance of the electrode interface to achieve higher signal to noise ratios. There are previous reports of accurate models for single-element biomedical electrodes. In this paper we develop a model for the electrode-electrolyte interface for tripolar concentric ring electrodes (TCRE) that are used to record brain signals.

A review of electrodes for the electrical brain signal recording

Brain is complex organ composed of numerous glial cells and neurons to convey information using chemical and electrical signals. Neural interface technology using the electrical brain signals has attracted great attention for the clinical and experimental applications. Electrode as the neural interface is the most important part in stimulating neural cells or recording neural activities. In this paper, we provide an overview of electrodes for recording the electrical brain signal. The noninvasive electrodes are primarily used to capture electroencephalogram (EEG) from outside the skull while the implantable electrodes are employed to measure electrocorticogram (ECoG), local field potential (LFP) or spike activity. Recent progress in microfabrication technology enables the development of on-board electrode that combines the entire signal processing including amplification, filtering, and digitization. This will contribute to diagnostic and therapeutic application of the neural interface for restoring physical, psychological and social functions by improving motor, sensory or cognitive abilities.

Design and evaluation of area-efficient and wide-range impedance analysis circuit for multichannel high-quality brain signal recording system

To enable chronic and stable neural recording, we have been developing an implantable multichannel neural recording system with impedance analysis functions. One of the important things for high-quality neural signal recording is to maintain well interfaces between recording electrodes and tissues. We have proposed an impedance analysis circuit with a very small circuit area, which is implemented in a multichannel neural recording and stimulating system. In this paper, we focused on the design of an impedance analysis circuit configuration and the evaluation of a minimal voltage measurement unit. The proposed circuit has a very small circuit area of 0.23 mm2 designed with 0.18 µm CMOS technology and can measure interface impedances between recording electrodes and tissues in ultrawide ranges from 100 Ω to 10 MΩ. In addition, we also successfully acquired interface impedances using the proposed circuit in agarose gel experiments.