Low-cost electroencephalogram (EEG) based authentication

2011 ◽  
Author(s):  
Corey Ashby ◽  
Amit Bhatia ◽  
Francesco Tenore ◽  
Jacob Vogelstein
Keyword(s):  
Low Cost ◽  
Electroencephalogram Eeg

scholarly journals Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 988
Author(s):  
Ho-Seung Cha ◽  
Chang-Hee Han ◽  
Chang-Hwan Im
Keyword(s):  
Resting State ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Mental States ◽  
Computer Interface ◽  
Practical Applications ◽  
Eeg Data ◽  
Brain States ◽  
Electroencephalogram Eeg ◽  
Eeg Features

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

scholarly journals Electroencephalogram (EEG) Based Imagined Speech Decoding and Recognition

2021 ◽  
Vol 2 (2) ◽  
pp. 74-84
Author(s):  
Sani Saminu ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Abd El Kader Isselmou ◽  
Adamu Halilu Jabire ◽  
...  
Keyword(s):  
Neurological Disorders ◽  
Brain Activity ◽  
Low Cost ◽  
High Temporal Resolution ◽  
Human Cortex ◽  
Prosthetic Devices ◽  
Neurological Processes ◽  
Electroencephalogram Eeg ◽  
Neuroprosthetic Devices ◽  
Neuroimaging Techniques

The recent investigations and advances in imagined speech decoding and recognition has tremendously improved the decoding of speech directly from brain activity with the help of several neuroimaging techniques that assist us in exploring the neurological processes of imagined speech. This development leads to assist people with disabilities to benefit from neuroprosthetic devices that improve the life of those suffering from neurological disorders. This paper presents the summary of recent progress in decoding imagined speech using Electroenceplography (EEG) signal, as this neuroimaging method enable us to monitor brain activity with high temporal resolution, it is very portable, low cost, and safer as compared to other methods. Therefore, it is a good candidate in investigating an imagined speech decoding from the human cortex which remains a challenging task. The paper also reviews some recent techniques, challenges, future recommendations and possible solutions to improve prosthetic devices and the development of brain computer interface system (BCI).

scholarly journals Multimodal-Multisensory Experiments

2020 ◽  
Author(s):  
Moein Razavi ◽  
Takashi Yamauchi ◽  
Vahid Janfaza ◽  
Anton Leontyev ◽  
Shanle Longmire-Monford ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Response Time ◽  
Galvanic Skin Response ◽  
Low Cost ◽  
Body Movement ◽  
Human Mind ◽  
Body Motion ◽  
Mouse Cursor ◽  
Skin Response ◽  
Electroencephalogram Eeg

The human mind is multimodal. Yet most behavioral studies rely on century-old measures of behavior—task accuracy and latency (response time). Multimodal and multisensory analysis of human behavior creates a better understanding of how the mind works. The problem is that designing and implementing these experiments is technically complex and costly. This paper introduces versatile and economical means of developing multimodal-multisensory human experiments. We provide an experimental design framework that automatically integrates and synchronizes measures including electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, virtual reality (VR), body movement, mouse/cursor motion and response time. Unlike proprietary systems (e.g., iMotions), our system is free and open-source; it integrates PsychoPy, Unity and Lab Streaming Layer (LSL). The system embeds LSL inside PsychoPy/Unity for the synchronization of multiple sensory signals—gaze motion, electroencephalogram (EEG), galvanic skin response (GSR), mouse/cursor movement, and body motion—with low-cost consumer-grade devices in a simple behavioral task designed by PsychoPy and a virtual reality environment designed by Unity. This tutorial shows a step-by-step process by which a complex multimodal-multisensory experiment can be designed and implemented in a few hours. When conducting the experiment, all of the data synchronization and recoding of the data to disk will be done automatically.

Sensing and Monitoring of Epileptical Seizure Under IoT Platform

2020 ◽  
pp. 201-223
Author(s):  
Akash Kumar Gupta ◽  
Chinmay Chakraborty ◽  
Bharat Gupta
Keyword(s):  
Health Service ◽  
Real Time ◽  
Neurological Disorder ◽  
Low Cost ◽  
Wearable Devices ◽  
Real Time Monitoring ◽  
Digital World ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Health Field

Epilepsy is a disorder that affects the life of the patient. In this neurological disorder, patients may suffer from different types of seizures. From epileptic patients, we may acquire electroencephalogram (EEG) data using various kinds of sensors and transmit them through the cloud. In this chapter, the authors have discussed various platforms related to IoT-enabled cloud for sharing the information and to get quick response in form suggestion. Use of smartphone applications for real-time monitoring of patients and for other applications is presented here. Various wearable devices may provide huge benefits for taking care of seizures and patients. The authors proposed a system model based on IoT-enabled cloud for sharing the information with various sensors and other devices to make a proper judgment about seizures, which will be able to provide improved e-health service. With the increasing rate of improvement in both IoT and e-health field, it is now a challenge to upgrade ourselves and work with the digital world to provide low cost, accurate, and quick solutions.

scholarly journals Neurofeedback for Chronic Pain

2020 ◽  
Author(s):  
Kajal Patel ◽  
Manoj Sivan ◽  
James Henshaw ◽  
Anthony Jones
Keyword(s):  
Chronic Pain ◽  
Brain Activity ◽  
Low Cost ◽  
Holistic Approach ◽  
Home Setting ◽  
Central Processing ◽  
Eeg Neurofeedback ◽  
Alpha Beta ◽  
Sensorimotor Rhythms ◽  
Electroencephalogram Eeg

Neurofeedback is a novel neuromodulatory therapy where individuals are given real-time feedback regarding their brain neurophysiological signals in order to increase volitional control over their brain activity. Such biofeedback platform can be used to increase an individual’s resilience to pain as chronic pain has been associated with abnormal central processing of ascending pain signals. Neurofeedback can be provided based on electroencephalogram (EEG) or functional magnetic resonance imaging (fMRI) recordings of an individual. Target brain rhythms commonly used in EEG neurofeedback for chronic pain include theta, alpha, beta and sensorimotor rhythms. Such training has not only been shown to improve pain in a variety of pain conditions such as central neuropathic pain, fibromyalgia, traumatic brain injury and chemotherapy induced peripheral neuropathy, but has also been shown to improve pain associated symptoms such as sleep, fatigue, depression and anxiety. Adverse events associated with neurofeedback training are often self-limited and resolve with decreased frequency of training. Provision of such training has also been explored in the home setting whereby individuals have been encouraged to practice this as and when required with promising results. Therefore, neurofeedback has the potential to provide low-cost yet holistic approach to the management of chronic pain.

Temporal Window based Feature Extraction Technique for Motor-Imagery EEG Signal Classification

2021 ◽  
Author(s):  
Rajdeep Chatterjee ◽  
Ankita Datta ◽  
Debarshi Kumar Sanyal ◽  
Swati Banerjee
Keyword(s):  
Motor Imagery ◽  
Filter Bank ◽  
Prediction Models ◽  
Low Cost ◽  
Temporal Window ◽  
Data Set ◽  
Research Areas ◽  
Wavelet Energy Entropy ◽  
Electroencephalogram Eeg ◽  
Bagging Ensemble

ABSTRACTElectroencephalogram (EEG) based motor-imagery classification is one of the most popular Brain Computer Interface (BCI) research areas due to its portability and low cost. In this paper, we have compared Wavelet Energy-entropy based different prediction models and empirically proven that temporal window based approach in motor-imagery classification provides more consistent and better results than popular filter-bank approach. In order to examine the robustness and stability of the proposed method, we have also employed multiple types of classifiers at the end and found that mix-bagging (bagging ensemble learning with multiple types of learners) technique out-smarts other frequently used classifiers. In our study, BCI Competition II Data-set III has been used with four experimental setup: (a) The whole signal (for each trial) as one segment, (b) The whole signal (for each trial) is divided into non-overlapping segments, (c) The whole signal (for each trial) is divided into overlapping segments, and (d) The filter-bank approach where the whole signal (each trial) is segmented based on different frequency bands. The result obtained from the experiment (c) i.e. 91.43% classification accuracy which outperforms the other approaches not only in this paper but to best of our knowledge it is the highest performance for this dataset so far.

scholarly journals EEG Biomarker for Alzheimer’s Disease

2020 ◽  
Author(s):  
Demet Ilhan Algin ◽  
Demet Ozbabalık Adapinar ◽  
Oguz Osman Erdinc
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Low Cost ◽  
Cost Effective ◽  
Event Related Potential ◽  
Cortical Atrophy ◽  
Mri Findings ◽  
Magnetic Resonance Imaging Mri ◽  
Electroencephalogram Eeg

Alzheimer’s disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 50 million dementia cases estimated worldwide. There is no cure for AD. Currently, AD diagnosis is carried out using neuropsychological tests, neuroimaging scans, and laboratory tests. In the early stages of AD, brain computed tomography (CT) and magnetic resonance imaging (MRI) findings may be normal, but in late periods, diffuse cortical atrophy can be detected more prominently in the temporal and frontal regions. Electroencephalogram (EEG) is a test that records the electrical signals of the brain by using electrodes that directly reflects cortical neuronal functioning. In addition, EEG is noninvasive and widely available at low cost, has high resolution, and provides access to neuronal signals, unlike functional MR or PET which indirectly detects metabolic signals. Accurate, specific, and cost-effective biomarkers are needed to track the early diagnosis, progression, and treatment response of AD. The findings of EEG in AD are now identified as biomarkers. In this chapter, we reviewed studies that used EEG or event-related potential (ERP) indices as a biomarker of AD.

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