Current Practices in Electroencephalogram- Based Brain-Computer Interfaces

2011 ◽  
pp. 888-901 ◽  
Author(s):  
Ramaswamy Palaniappan ◽  
Chanan S. Syan ◽  
Raveendran Paramesran
Keyword(s):  
Electrical Activity ◽  
Communication Systems ◽  
External Environment ◽  
Assistive Technologies ◽  
Memory Impairments ◽  
Menu Selection ◽  
Scalp Eeg ◽  
Computer Interfaces ◽  
Electroencephalogram Eeg ◽  
The Brain

Electroencephalogram (EEG) is the electrical activity of the brain recorded by electrodes placed on the scalp. EEG signals are generally investigated for the diagnosis of mental conditions such as epilepsy, memory impairments, and sleep disorders. In recent years there has been another application using EEG: for brain-computer interface (BCI) designs (Vaughan & Wolpaw, 2006). EEG-based BCI designs are very useful for hands-off device control and communication as they use the electrical activity of the brain to interface with the external environment, therefore circumventing the use of peripheral muscles and limbs. Some current applications of BCIs in communication systems are for paralyzed individuals to communicate with their surroundings through character/menu selection and in device control such as wheelchair movement, prosthetics control, and flight and rehabilitative (assistive) technologies. For the general public, some of the possible applications are hands-off menu selection, flight/space control, and virtual reality (entertainment). BCI has also been applied in biometrics (Palaniappan & Mandic, 2007).

Nonlinear Approach to Brain Signal Modeling

2011 ◽  
pp. 2834-2839
Author(s):  
Tugce Balli ◽  
Ramaswamy Palaniappan
Keyword(s):  
Signal Processing ◽  
Electrical Activity ◽  
Nonlinear Modeling ◽  
Biological Mechanisms ◽  
Linear Modeling ◽  
Memory Impairments ◽  
Biological Signal ◽  
Biological Signals ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

Biological signal is a common term used for time series measurements that are obtained from biological mechanisms and basically represent some form of energy produced by the biological mechanisms. Examples of such signals are electroencephalogram (EEG), which is the electrical activity of brain recorded by electrodes placed on the scalp; electrocardiogram (ECG), which is electrical activity of heart recorded from chest, and electromyogram (EMG), which is recorded from skin as electrical activity generated by skeletal muscles (Akay, 2000). Nowadays, biological signals such as EEG and ECG are analysed extensively for diagnosing conditions like cardiac arrhythmias in the case of ECG and epilepsy, memory impairments, and sleep disorders in case of EEG. Apart from clinical diagnostic purposes, in recent years there have been many developments for utilising EEG for brain computer interface (BCI) designs (Vaughan & Wolpaw, 2006). The field of signal processing provides many methods for analysis of biological signals. One of the most important steps in biological signal processing is the extraction of features from the signals. The assessment of such information can give further insights to the functioning of the biological system. The selection of proper methods and algorithms for feature extraction (i.e., linear/nonlinear methods) are current challenges in the design and application of real time biological signal analysis systems. Traditionally, linear methods are used for the analysis of biological signals (mostly in analysis of EEG). Although the conventional linear analysis methods simplify the implementation, they can only give an approximation to the underlying properties of the signal when the signal is in fact nonlinear. Because of this, there has been an increasing interest for utilising nonlinear analysis techniques in order to obtain a better characterisation of the biological signals. This chapter will lay the backgrounds to linear and nonlinear modeling of EEG signals, and propose a novel nonlinear model based on exponential autoregressive (EAR) process, which proves to be superior to conventional linear modeling techniques.

EEG Analysis of Imagined Speech

2020 ◽  
pp. 679-692
Author(s):  
Sadaf Iqbal ◽  
Muhammed Shanir P.P. ◽  
Yusuf Uzzaman Khan ◽  
Omar Farooq
Keyword(s):  
Mean Absolute Deviation ◽  
Eeg Analysis ◽  
Absolute Deviation ◽  
Scalp Eeg ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Scalp Electroencephalogram ◽  
Electroencephalogram Eeg ◽  
Control State ◽  
Excellent Tool

Scalp electroencephalogram (EEG) is one of the most commonly used methods to acquire EEG data for brain-computer interfaces (BCIs). Worldwide a large number of people suffer from disabilities which impair normal communication. Communication BCIs are an excellent tool which helps the affected patients communicate with others. In this paper scalp EEG data is analysed to discriminate between the imagined vowel sounds /a/, /u/ and no action or rest as control state. Mean absolute deviation (MAD) and Arithmetic mean are used as features to classify data into one of the classes /a/, /u/ or rest. With high classification accuracies of 87.5-100% for two class problem and 78.33-96.67% for three class problem that have been obtained in this work, this algorithm can be used in communication BCIs, to develop speech prosthesis and in synthetic telepathy systems.

4 Brain computer interface for paralysis

2021 ◽  
Vol 92 (8) ◽  
pp. A1.4-A2
Author(s):  
Leigh R Hochberg
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Assistive Technologies ◽  
Computer Interfaces ◽  
Cord Injury ◽  
Challenges And Opportunities ◽  
Communication Devices ◽  
Intention To Move ◽  
The Brain ◽  
Made In

Intracortically-based Brain-Computer Interfaces (iBCIs) are poised to revolutionize our ability to restore lost neurologic functions. By recording high resolution neural activity from the brain, the intention to move ones hand can be detected and decoded in real- time, potentially providing people with motor neuron disease (ALS), stroke, or spinal cord injury with restored or maintained ability to control communication devices, assistive technologies, and their own limbs. iBCIs also are central to the development of closed-loop neuromodulation systems, with great potential to serve people with neuropsychiatric disorders. A multi-site pilot clinical trial of the investigational BrainGate system is assessing the feasibility of people with tetraplegia controlling a computer cursor and other devices simply by imagining the movement of their own arm or hand. This presentation will review some of the recent progress made in iBCIs, the information that can be decoded from ensembles of cortical or subcortical neurons in real-time, and the challenges and opportunities for restorative neurotechnologies in research and clinical practice.

EEG Analysis of Imagined Speech

2016 ◽  
Vol 3 (2) ◽  
pp. 32-44
Author(s):  
Sadaf Iqbal ◽  
Muhammed Shanir P.P. ◽  
Yusuf Uzzaman Khan ◽  
Omar Farooq
Keyword(s):  
Mean Absolute Deviation ◽  
Eeg Analysis ◽  
Absolute Deviation ◽  
Scalp Eeg ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Scalp Electroencephalogram ◽  
Electroencephalogram Eeg ◽  
Control State ◽  
Excellent Tool

Scalp electroencephalogram (EEG) is one of the most commonly used methods to acquire EEG data for brain-computer interfaces (BCIs). Worldwide a large number of people suffer from disabilities which impair normal communication. Communication BCIs are an excellent tool which helps the affected patients communicate with others. In this paper scalp EEG data is analysed to discriminate between the imagined vowel sounds /a/, /u/ and no action or rest as control state. Mean absolute deviation (MAD) and Arithmetic mean are used as features to classify data into one of the classes /a/, /u/ or rest. With high classification accuracies of 87.5-100% for two class problem and 78.33-96.67% for three class problem that have been obtained in this work, this algorithm can be used in communication BCIs, to develop speech prosthesis and in synthetic telepathy systems.

scholarly journals An Investigation on the Electrical Activity of the Brain during Fara-Darmani Connection in the Fara-Therapist Population

2020 ◽  
Author(s):  
Mohammad Ali Taheri ◽  
Farid Semsarha ◽  
Fateme Modarresi-Asem
Keyword(s):  
Electrical Activity ◽  
Human Mind ◽  
Intrinsic Activity ◽  
Brain Functioning ◽  
Body Interaction ◽  
Cortical Electrical Activity ◽  
Rest Condition ◽  
The Mind ◽  
Electroencephalogram Eeg ◽  
The Brain

Mind-body interaction and its manifestations at the brain level has been studied extensively in the field of consciousness research. Fara-darmani Consciousness Field, as claimed by Mohammad Ali Taheri (the founder), is a method of connecting with the Cosmic Consciousness Network through human mind and his brain has a detective role in this process. As a result of this connection, the scanning process of the state of a being, e.g., the health status of the cells and consequently organs is performed. This study was conducted to evaluate the effects of the Fara-darmani Consciousness Field connection on electroencephalogram (EEG) features as an important biomarker of the brain functioning. The results showed that there was a significant increase in the gamma2 frequency band (35-40 Hz) power in the frontal lobe in medial frontal gyrus (BA6) and paracentral lobule (BA31) of the brain during the task condition compared to the rest condition in a Fara-therapist population. Considering the cortical electrical activity of Fara-therapist’s brain during Fara-darmani Consciousness Field connection, characterizing increase in the power of gamma wave and the activity of the areas affecting on memory, attention, perception and default mode network intrinsic activity. This manifestation distinguishes Fara-darmani Consciousness Field connection from other known methods dealing with the mind-body interaction criterion mainly different types of mediation.

DETECTION OF THE ONSET OF EPILEPTIC SEIZURE SIGNAL FROM SCALP EEG USING BLIND SIGNAL SEPARATION

2009 ◽  
Vol 21 (04) ◽  
pp. 287-290 ◽  
Author(s):  
M. Moghavvemi ◽  
S. Mehrkanoon
Keyword(s):  
Signal Separation ◽  
Blind Signal Separation ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Blind Signal ◽  
Scalp Eeg ◽  
Mapping Process ◽  
Independent Features ◽  
Electroencephalogram Eeg ◽  
The Brain

Investigation of epileptic electroencephalogram (EEG) signal is one of the major areas of study in the field of signal processing. The ability to detect the seizure signal and its origin within the brain is of prime importance. This paper proposes a sequential blind signal separation (BSS) based system to extract the seizure signal from scalp EEG and to pinpoint the main location of seizure signal within the brain. BSS algorithm is used to demix the EEG signal into signals with independent features. Scalp time-mapping process is applied to determine the main location of the extracted seizure signal within the brain. The algorithm has been tested on epileptic EEG signals recorded from patients for detection of the onset of seizure waves and their origin within the brain.

Generalized Information Measures and the Analysis of Brain Electrical Signals

2004 ◽  
Author(s):  
A. Plastino ◽  
M. T. Martin
Keyword(s):  
Fourier Transform ◽  
Electrical Activity ◽  
Brain Activity ◽  
Quantitative Eeg ◽  
Complexity Measures ◽  
Information Measures ◽  
Information Tools ◽  
The Fourier Transform ◽  
Electroencephalogram Eeg ◽  
The Brain

The traditional way of analyzing brain electrical activity, on the basis of electroencephalogram (EEG) records, relies mainly on visual inspection and years of training. Although it is quite useful, of course, one has to acknowledge its subjective nature that hardly allows for a systematic protocol. In order to overcome this undesirable feature, a quantitative EEG analysis has been developed over the years that introduces objective measures. These reflect not only characteristics of the brain activity itself, but also clues concerning the underlying associated neural dynamics. The processing of information by the brain is reflected in dynamical changes of the electrical activity in (i) time, (ii) frequency, and (iii) space. Therefore, the concomitant studies require methods capable of describing the qualitative variation of the signal in both time and frequency. In the present work we introduce new information tools based on the wavelet transform for the assessment of EEG data. In particular, different complexity measures are utilized…. The traditional electroencephalogram (EEG) tracing is now interpreted in much the same way as it was 50 years ago. More channels are used now and much more is known about clinical implication of the waves, but the basic EEG display and quantification of it are quite similar to those of its predecessors. The clinical interpretation of EEG records is made by a complex process of visual pattern recognition and the association with external and evident characteristics of clinical symptomatology. Analysis of EEG signals always involves the queries of quantification, i.e., the ability to state objective data in numerical and/or graphic form that simplify the analysis of long EEG time series. Without such measures, EEG appraisal remains subjective and can hardly lead to logical systematization [36]. Spectral decomposition of the EEG by computing the Fourier transform has been used since the very early days of electroencephalography. The rhythmic nature of many EEG activities lends itself naturally to this analysis. Fourier transform allows separation of various rhythms and estimation of their frequencies independently of each other, a difficult task to perform visually if several rhythmic activities occur simultaneously. Spectral analysis can also quantify the amount of activity in a frequency band.

Electroencephalogram indices in patients undergoing cardiac arrest

2020 ◽  
Vol 1 (14) ◽  
pp. 32-38
Author(s):  
I. Yu. Berezina ◽  
L. I. Sumsky ◽  
A. Yu. Mikhailov ◽  
Yu. L. Arzumanov
Keyword(s):  
Cardiac Arrest ◽  
Electrical Activity ◽  
Paroxysmal Activity ◽  
Equivalent Dipole ◽  
Neurophysiological Mechanisms ◽  
Eeg Changes ◽  
Eeg Recordings ◽  
The Moment ◽  
Electroencephalogram Eeg ◽  
The Brain

Objective: to assess the safety of indicators of electrical activity of the brain for the approach to the analysis of the basic neurophysiological mechanisms of the brain in patients after cardiac arrest.Materials and methods: 52 patients were examined (age — 54,68 ± 19,33) after cardiac arrest. At the time of recording the electroencephalogram (EEG), the level of wakefulness of the examined patients on the Glasgow coma scale was in the range of 3 to 13 points. In 35 patients, EEG recording was performed starting from the first three days from the moment of cardiac arrest, in 17 patients — from the fourth to the 18th day. EEG was registered on electroencephalographs ‘Encephalan–EEGR–19/26’ by ‘Medikom MTD’, ‘Neuron-Spectrum–5/EP’ and ‘Neuron-Spectrum–65’ by ‘Neurosoft’ in accordance with the recommendations of the International Federation of Clinical Neurophysiologists (IFCN). The duration of a single EEG recordings lasted at least 30 min. To localize equivalent dipole sources of pathological activity we used the program ‘BrainLoc 6.0’, (Russia). In 19 patients EEG was recorded in dynamics from 2 to 8 times.Results: all patients showed EEG changes of varying severity, which can be divided into three groups (according to the severity of changes in the EEG: moderate, severe and rough). In the group of patients with gross changes in EEG can be identified 4 variants: the first variant — absence of the alpha rhythm and the dominance of slow-wave fluctuations of the frequency spectrum; variant II — continuous generalized paroxysmal activity; variant III — phenomenon of ‘burst-suppression’; variant IV — a marked decrease in the amplitude of electrical activity of the brain to the level of 2–4 microvolt.Conclusions: based on the dynamics of the EEG pattern in patients after cardiac arrest, it is possible to assume with a certain degree of probability the level of violations in the basic mechanisms of the brain.

scholarly journals Analyzing Passive BCI Signals to Control Adaptive Automation Devices

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3042 ◽  
Author(s):  
Ghada Al-Hudhud ◽  
Layla Alqahtani ◽  
Heyam Albaity ◽  
Duaa Alsaeed ◽  
Isra Al-Turaiki
Keyword(s):  
Adaptive Control ◽  
Control Model ◽  
Assistive Technologies ◽  
Eeg Signals ◽  
Adaptive Automation ◽  
Computer Interfaces ◽  
Comfort Levels ◽  
Movement Artifacts ◽  
Increase Productivity ◽  
Electroencephalogram Eeg

Brain computer interfaces are currently considered to greatly enhance assistive technologies and improve the experiences of people with special needs in the workplace. The proposed adaptive control model for smart offices provides a complete prototype that senses an environment’s temperature and lighting and responds to users’ feelings in terms of their comfort and engagement levels. The model comprises the following components: (a) sensors to sense the environment, including temperature and brightness sensors, and a headset that collects electroencephalogram (EEG) signals, which represent workers’ comfort levels; (b) an application that analyzes workers’ feelings regarding their willingness to adjust to a space based on an analysis of collected data and that determines workers’ attention levels and, thus, engagement; and (c) actuators to adjust the temperature and/or lighting. This research implemented independent component analysis to remove eye movement artifacts from the EEG signals and used an engagement index to calculate engagement levels. This research is expected to add value to research on smart city infrastructures and on assistive technologies to increase productivity in smart offices.

scholarly journals Application of Deep Learning for Emotion Recognition

YMER Digital ◽  
2021 ◽  
Vol 20 (12) ◽  
pp. 834-840
Author(s):  
Varsha R Toshniwal ◽  
◽  
Pooja S Puri ◽  
Keyword(s):  
Network Architecture ◽  
Emotional State ◽  
Human Perception ◽  
Brain Regions ◽  
Eeg Signals ◽  
Neural Network Architecture ◽  
Computer Interfaces ◽  
Alpha Beta ◽  
Electroencephalogram Eeg ◽  
The Brain

The electroencephalogram (EEG) gained a lot of importance in recent years because of its property to depict the nature and actions of human perception. EEG signals are good at capturing the emotional state of a person by measuring the neuronal activities in different regions of the brain. Lots of EEG-based brain-computer interfaces with a different number of channels ( 62 channels, 32 channels, etc.) are being used to capture neuronal activities which can be segmented into different frequency ranges (delta, theta, alpha. beta and gamma). This paper puts forward a neural network architecture for the recognition of emotion from EEG signals and a study providing the set of brain regions and the frequency type associated with the corresponding brain region which contributes most for the detection of emotion though EEG signals. For experimentation, SEED-IV dataset has been used

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