scholarly journals Thoughts on neurophysiological signal analysis and classification

2020 ◽  
Vol 6 (3) ◽  
pp. 210-223
Author(s):  
Junhua Li
Keyword(s):  
Signal Analysis ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Brain Diseases ◽  
Non Invasive ◽  
Cognitive States ◽  
Task Classification ◽  
New Ideas ◽  
Electroencephalogram Eeg

Neurophysiological signals are crucial intermediaries, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, non‐invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcomed and frequently utilised in various studies since these signals can be non‐invasively recorded without harming the human brain while they convey abundant information pertaining to brain activity. The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns (e.g., different cognitive states, brain diseases versus healthy controls). To date, remarkable progress has been made in both the analysis and classification of neurophysiological signals, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signals. In this paper, I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments. I will elucidate the thoughts after brief summaries of relevant backgrounds, accomplishments, and tendencies. According to my personal selection and preference, I mainly focus on brain connectivity, multidimensional array (tensor), multi‐modality, multiple task classification, deep learning, big data, and naturalistic experiment. Hopefully, my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.

scholarly journals A comparative study on feature extraction and classification of mind waves for brain computerinterface (BCI)

2018 ◽  
Vol 7 (1.9) ◽  
pp. 132
Author(s):  
Manjula K ◽  
M B.Anandaraju
Keyword(s):  
Feature Extraction ◽  
Brain Activity ◽  
Non Invasive ◽  
Brain Signals ◽  
Control Command ◽  
Signal Processing Algorithms ◽  
Brain Computer Interfacing ◽  
Electroencephalogram Eeg ◽  
Bci System

Brain Computer Interfacing (BCI) is a methodology which imparts a path for communication from external world using brain signals through computer. BCI identifies the specific patterns in a person’s changing brain activity to initiate control which relates to the person’s intention. The BCI system paraphrases these signal patterns into meaningful control command. In evolving BCI system, numerous signal processing algorithms are proposed. Non-invasive Electroencephalogram (EEG) signals or mind waves are used to extract the distinguished features and further they are classified choosing an appropriate classifier. A study on different feature extraction & Classification algorithms is used in EEG-based BCI exploration and to identify their distinct properties. This paper proposes different methodologies of feature extraction and feature Classification. It also addresses the methods and technology adapted in every phase of the EEG signal processing.This comparative survey also helps in selecting suitable algorithm for the development and accomplishment of further classification of signals.

Closed-Loop Control of the Frequency Response of the Virtual Brain Model

2017 ◽  
Author(s):  
Christine Beauchene ◽  
Alexander Leonessa ◽  
Subhradeep Roy ◽  
James Simon ◽  
Nicole Abaid
Keyword(s):  
Large Scale ◽  
Closed Loop ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Closed Loop Control ◽  
Loop Control ◽  
Non Invasive ◽  
Frequency Mismatch ◽  
Stimulation Method ◽  
The Difference

The brain is a highly complex network and analyzing brain connectivity is a nontrivial task. Consequently, the neuroscience community created a large-scale, customizable, mathematical model which simulates brain activity called The Virtual Brain (TVB). Using TVB, we seek to control electroencephalography (EEG) measured brain states using auditory inputs, through TVB. A safe non-invasive brain stimulation method is binaural beats (BB) which arise from the brain’s interpretation of two pure tones, with a small frequency mismatch, delivered independently to each ear. A third phantom BB, whose frequency is equal to the difference of the two presented tones, is produced. This paper details the development and proof-of-concept testing of a simulation environment for an EEG-based closed-loop control of TVB using BB. Results suggest that the connectivity networks, constructed from simulated EEG, may change with certain BB stimulation frequency. In this work, we demonstrate that a linear controller can successfully modulate TVB connectivity.

scholarly journals Introduction to the identification of brain waves based on their frequency

2018 ◽  
Vol 210 ◽  
pp. 05012 ◽  
Author(s):  
Zuzana Koudelková ◽  
Martin Strmiska
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Brain Wave ◽  
Non Invasive ◽  
Brain Waves ◽  
Analytical Reasoning ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Research Type

A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.

scholarly journals Transcranial magnetic stimulation: studying the brain--behaviour relationship by induction of ‘virtual lesions’

1999 ◽  
Vol 354 (1387) ◽  
pp. 1229-1238 ◽  
Author(s):  
Alvaro Pascual-Leone
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Causal Link ◽  
Non Invasive ◽  
Invasive Method ◽  
Normal Humans ◽  
The Brain ◽  
Neuroimaging Techniques

Transcranial magnetic stimulation (TMS) provides a non-invasive method of induction of a focal current in the brain and transient modulation of the function of the targeted cortex. Despite limited understanding about focality and mechanisms of action, TMS provides a unique opportunity of studying brain-behaviour relations in normal humans. TMS can enhance the results of other neuroimaging techniques by establishing the causal link between brain activity and task performance, and by exploring functional brain connectivity.

scholarly journals Study of Metrics that Could be Considered as Inputs to an Intelligent System for Diagnosing Schizophrenia based on an Electroencephalogram (EEG)

2020 ◽  
Vol 5 (3) ◽  
pp. 292-296
Author(s):  
Pélagie Flore Temgoua Nanfack ◽  
Angouah Massaga Morel Junior
Keyword(s):  
Spectral Analysis ◽  
Frequency Band ◽  
Adjacency Matrix ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Brain Connectivity ◽  
Healthy People ◽  
Brain Diseases ◽  
Phase Lag ◽  
Electroencephalogram Eeg

Intelligent systems are now part of human daily life. This justifies its application in various fields. However, the field of psychiatry still seems to be at a disadvantage. In this paper, we are interested in measures that can be used as input to an intelligent system for detecting brain diseases using an electroencephalogram (EEG). Indeed, spectral analysis and the study of brain connectivity are two methods of EEG analysis that can be used to characterize schizophrenia. The spectral analysis allows to calculate the power (absolute and relative), the frequency peak among others. Concerning the study of cerebral connectivity, the Phase Lag Index (PLI), which is an adjacency matrix ; which is an adjacency matrix used to assess brain connectivity. Once the PLI is obtained, units such as degree, density and strength on each channel are calculated. These units are evaluated on twenty (28) EEGs, fourteen (14) of people suffering from schizophrenia and fourteen (14) of healthy people. Once the PLI is obtained, units such as degree, density and strength on each channel are calculated. These units are evaluated on twenty (28) EEGs, fourteen (14) of people suffering from schizophrenia and fourteen (14) of healthy people. On the other hand, the value of strength is always lower in sick people than in healthy people. This is true regardless of the frequency band or channel used. This study shows that values such as degree, density, strength of a predefined adjacency matrix, then power, peak frequency band can be used as input values of an intelligent system for diagnosing psychiatric or brain diseases such as schizophrenia.

Classification of Body Position During Muslim Prayer Using the Convolutional Neural Network

2021 ◽  
pp. 2154028
Author(s):  
Vahid Sobhani ◽  
Koorosh Izadi ◽  
Ehsan Manshadi Mokari ◽  
Boshra Hatef
Keyword(s):  
Cross Correlation ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Body Position ◽  
The Body ◽  
Suitable Model ◽  
Mutual Correlation ◽  
Delta Band ◽  
The Brain

Background: Muslim prayer (Namaz) is the most important obligatory religious duty in Islam that is regularly performed five times per day at specific prescribed times by Muslims. Due to the fact that change of body position affects brain activity, Namaz can be considered as a suitable model to assess the effect of quick changes of the body position on brain activity measured by electroencephalography (EEG). Methods: Forty Muslim participants performed a four-cycle Namaz while their brain activity was being recorded using a 14-channel EEG recorder. The brain connectivity (as defined by a mutual correlation between EEG channels in this study) in different frequency bands (delta, theta, alpha, beta, and gamma) was measured in various positions of Namaz including standing, bowing, prostration, and sitting. Results: The results indicated that the delta band demonstrates the most changes in cross-correlation between the recorded channels, and finally, the accuracy of 73.8% was obtained in the data classification.

scholarly journals Design of Brain Activity Measurement for Brain ECVT Data Acquisition System

2021 ◽  
Vol 6 (10) ◽  
pp. 630-634
Author(s):  
Rizki Edmi Edison ◽  
Rohmadi Rohmadi ◽  
Sra Harke Pratama ◽  
Muhammad Fathul Ihsan ◽  
Almusfi Saputra ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Brain Activity ◽  
Activity Measurement ◽  
Imaging Method ◽  
Capacitance Sensor ◽  
Non Invasive ◽  
Power Spectral ◽  
Activity Measurements ◽  
Electrical Capacitance Volume Tomography ◽  
The Brain

Brain Electrical Capacitance Volume Tomography (ECVT) has been developing as an alternative non-invasive brain imaging method. In this study, brain ECVT consisting of two channels, namely a capacitance sensor, is investigated. As a comparison, EEG sensor is used to measure brain activity simultaneously with the brain ECVT. Brain activity measurements were carried out at the pre-frontal lobe of Fp1 and Fp2 locations. The resulting signal was processed by filtering method and Power Spectral Density (PSD). The result of signal analysis shows that the measurement between EEG and ECVT shows the same activity of the two modalities.

scholarly journals Algorithms for Classification of Signals Derived From Human Brain

2021 ◽  
Vol 15 ◽  
pp. 1521-1526
Author(s):  
Georgi P. Dimitrov ◽  
Galina Panayotova ◽  
Boyan Jekov ◽  
Pavel Petrov ◽  
Iva Kostadinova ◽  
...  
Keyword(s):  
Random Forest ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Signal Acquisition ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Non Invasive ◽  
Basic Parameters ◽  
Linear Svm

Comparison of the Accuracy of different off-line methods for classification Electroencephalograph (EEG) signals, obtained from Brain-Computer Interface (BCI) devices are investigated in this paper. BCI is a technology that allows people to interact directly or indirectly with their environment only by using brain activity. But, the method of signal acquisition is non-invasive, resulting in significant data loss. In addition, the received signals do not contain only useful information. All this requires careful selection of the method for the classification of the received signals. The main purpose of this paper is to provide a fair and extensive comparison of some commonly employed classification methods under the same conditions so that the assessment of different classifiers will be more convictive. In this study, we investigated the accuracy of the classification of the received signals with classifiers based on AdaBoost (AB), Decision Tree (DT), k-Nearest Neighbor (kNN), Gaussian SVM, Linear SVM, Polynomial SVM, Random Forest (RF), Random Forest Regression ( RFR ). We used only basic parameters in the classification, and we did not apply fine optimization of the classification results. The obtained results show suitable algorithms for the classification of EEG signals. This would help young researchers to achieve interesting results in this field faster.

scholarly journals A Method to Extract P300 EEG Signal Feature Using Independent Component Analysis (ICA) for Lie Detection

2017 ◽  
Vol 2 (1) ◽  
pp. 9
Author(s):  
Wahyu Caesarendra
Keyword(s):  
Independent Component Analysis ◽  
Brain Activity ◽  
Lie Detection ◽  
Component Analysis ◽  
Independent Component ◽  
Non Invasive ◽  
Brain Signals ◽  
Multiple Electrodes ◽  
Almost All

The progress of today's technology is growing very quickly. This becomes the motivation for the community to be able to continue and provide innovations. One technology to be developed is the application of brain signals or called with electroencephalograph (EEG). EEG is a non-invasive measurement method that represents electrical signals from brain activity obtained by placement of multiple electrodes on the scalp in the area of the brain, thus obtaining information on electrical brain signals to be processed and analyzed. Lie is an act of covering up something so that only the person who is lying knows the truth of the statement. The hidden information from lying subjects will elicit an EEG-P300 signal response using Independent Component Analysis (ICA) in different shapes of amplitude that tends to be larger around 300 ms after stimulation. The method used in the experiment is to invite subject in a card game so that the process can be done naturally and the subject can well stimulated. After the trials there are several results almost all subjects have the same frequency on the frequency of 24-27 Hz. This is a classification of beta waves that have a frequency of 13-30 Hz where the beta wave is closely related to active thinking and attention, focusing on the outside world or solving concrete problems.

scholarly journals Transcranial Focused Ultrasound Neuromodulation: A Review of the Excitatory and Inhibitory Effects on Brain Activity in Human and Animals

2021 ◽  
Vol 15 ◽  
Author(s):  
Tingting Zhang ◽  
Na Pan ◽  
Yuping Wang ◽  
Chunyan Liu ◽  
Shimin Hu
Keyword(s):  
Focused Ultrasound ◽  
Brain Activity ◽  
Pressure Effects ◽  
Spinal Reflexes ◽  
Brain Diseases ◽  
Inhibitory Effects ◽  
Mechanical Pressure ◽  
Non Invasive ◽  
Non Destructive ◽  
Transcranial Focused Ultrasound

Non-invasive neuromodulation technology is important for the treatment of brain diseases. The effects of focused ultrasound on neuronal activity have been investigated since the 1920s. Low intensity transcranial focused ultrasound (tFUS) can exert non-destructive mechanical pressure effects on cellular membranes and ion channels and has been shown to modulate the activity of peripheral nerves, spinal reflexes, the cortex, and even deep brain nuclei, such as the thalamus. It has obvious advantages in terms of security and spatial selectivity. This technology is considered to have broad application prospects in the treatment of neurodegenerative disorders and neuropsychiatric disorders. This review synthesizes animal and human research outcomes and offers an integrated description of the excitatory and inhibitory effects of tFUS in varying experimental and disease conditions.

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