scholarly journals Hardware Design of a Tele-EEG Device For Detection of Neurophysiological Condition

2021 ◽  
Author(s):  
Pouya Aminaie
Keyword(s):  
Mobile Phone ◽  
Medical Center ◽  
Hardware Design ◽  
Main Idea ◽  
Health Condition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Interface System ◽  
Bci System ◽  
At Home

The main idea of this research originates from the patients such that each patient with neural disorder should refer to a medical center to check his brain's health condition to sample his EEG signals and present the results to a specialist for further investigation. If this process can be done remotely by tele-medicine techniques, it will save cost and time. In tele-medicine method, the patient can record the EEG signal alone at home and send the results to his physician. To this end, this research employs Bluetooth to connect the interface system to the computer, and the patient can send the results to his physician after saving the data. Thus, the main purpose of designing this BCI system is to record EEG signals using a microcontroller and transmit them via Bluetooth to a computer and mobile phone such that the signal can be represented instantaneously in a GUI.

scholarly journals Hardware Design of a Tele-EEG Device For Detection of Neurophysiological Condition

2021 ◽  
Author(s):  
Pouya Aminaie ◽  
Poorya Aminaie
Keyword(s):  
Mobile Phone ◽  
Medical Center ◽  
Hardware Design ◽  
Main Idea ◽  
Health Condition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Interface System ◽  
Bci System ◽  
At Home

The main idea of this research originates from the patients such that each patient with neural disorder should refer to a medical center to check his brain's health condition to sample his EEG signals and present the results to a specialist for further investigation. If this process can be done remotely by tele-medicine techniques, it will save cost and time. In tele-medicine method, the patient can record the EEG signal alone at home and send the results to his physician. To this end, this research employs Bluetooth to connect the interface system to the computer, and the patient can send the results to his physician after saving the data. Thus, the main purpose of designing this BCI system is to record EEG signals using a microcontroller and transmit them via Bluetooth to a computer and mobile phone such that the signal can be represented instantaneously in a GUI.

scholarly journals Hardware Design of a Tele-EEG Device For Detection of Neurophysiological Condition

2021 ◽  
Author(s):  
Pouya Aminaie ◽  
Poorya Aminaie
Keyword(s):  
Mobile Phone ◽  
Medical Center ◽  
Hardware Design ◽  
Main Idea ◽  
Health Condition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Interface System ◽  
Bci System ◽  
At Home

The main idea of this research originates from the patients such that each patient with neural disorder should refer to a medical center to check his brain's health condition to sample his EEG signals and present the results to a specialist for further investigation. If this process can be done remotely by tele-medicine techniques, it will save cost and time. In tele-medicine method, the patient can record the EEG signal alone at home and send the results to his physician. To this end, this research employs Bluetooth to connect the interface system to the computer, and the patient can send the results to his physician after saving the data. Thus, the main purpose of designing this BCI system is to record EEG signals using a microcontroller and transmit them via Bluetooth to a computer and mobile phone such that the signal can be represented instantaneously in a GUI.

scholarly journals Hardware Design of a Tele-EEG Device For Detection of Neurophysiological Condition

2021 ◽  
Author(s):  
Pouya Aminaie ◽  
Poorya Aminaie
Keyword(s):  
Mobile Phone ◽  
Medical Center ◽  
Hardware Design ◽  
Main Idea ◽  
Health Condition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Interface System ◽  
Bci System ◽  
At Home

The main idea of this research originates from the patients such that each patient with neural disorder should refer to a medical center to check his brain's health condition to sample his EEG signals and present the results to a specialist for further investigation. If this process can be done remotely by tele-medicine techniques, it will save cost and time. In tele-medicine method, the patient can record the EEG signal alone at home and send the results to his physician. To this end, this research employs Bluetooth to connect the interface system to the computer, and the patient can send the results to his physician after saving the data. Thus, the main purpose of designing this BCI system is to record EEG signals using a microcontroller and transmit them via Bluetooth to a computer and mobile phone such that the signal can be represented instantaneously in a GUI.

An SSVEP-Based BCI System for SMS in a Mobile Phone

2014 ◽  
Vol 513-517 ◽  
pp. 412-415 ◽  
Author(s):  
Jzau Sheng Lin ◽  
Mei Wang ◽  
Pei Yu Lia ◽  
Ze Jin Li
Keyword(s):  
Mobile Phone ◽  
Short Message Service ◽  
Computer Interface ◽  
Short Message ◽  
Visually Evoked Potentials ◽  
Eeg Signals ◽  
Mobile Platforms ◽  
Message Service ◽  
Als Patients ◽  
Bci System

In this paper, the purpose is to develop a Short Message Service (SMS) system that can aid the ALS patients to control a mobile phone to send a message by using of the steady state visually evoked potentials (SSVEP) brain computer interface (BCI) to a person who can help him/her. The EEG signals may be detected by electrodes and signal extracting chip. Then these signal can be transmitted to the mobile platforms by using of Bluetooth interface. Finally, the message and phone number can be selected in accordance with the components in frequency domain transferred from time domain. The experimental results had shown that the proposed system can easily send a message from a mobile phone to another.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

scholarly journals Risk factors associated with physician trainee concern over missed educational opportunities during the COVID-19 pandemic

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sunny S. Lou ◽  
Charles W. Goss ◽  
Bradley A. Evanoff ◽  
Jennifer G. Duncan ◽  
Thomas Kannampallil
Keyword(s):  
Risk Factors ◽  
Clinical Education ◽  
Medical Center ◽  
Clinical Care ◽  
Academic Medical Center ◽  
Clinical Work ◽  
Educational Opportunities ◽  
Missed Opportunities ◽  
Physician Trainee ◽  
At Home

Abstract Background The COVID-19 pandemic resulted in a transformation of clinical care practices to protect both patients and providers. These changes led to a decrease in patient volume, impacting physician trainee education due to lost clinical and didactic opportunities. We measured the prevalence of trainee concern over missed educational opportunities and investigated the risk factors leading to such concerns. Methods All residents and fellows at a large academic medical center were invited to participate in a web-based survey in May of 2020. Participants responded to questions regarding demographic characteristics, specialty, primary assigned responsibility during the previous 2 weeks (clinical, education, or research), perceived concern over missed educational opportunities, and burnout. Multivariable logistic regression was used to assess the relationship between missed educational opportunities and the measured variables. Results 22% (301 of 1375) of the trainees completed the survey. 47% of the participants were concerned about missed educational opportunities. Trainees assigned to education at home had 2.85 [95%CI 1.33–6.45] greater odds of being concerned over missed educational opportunities as compared with trainees performing clinical work. Trainees performing research were not similarly affected [aOR = 0.96, 95%CI (0.47–1.93)]. Trainees in pathology or radiology had 2.51 [95%CI 1.16–5.68] greater odds of concern for missed educational opportunities as compared with medicine. Trainees with greater concern over missed opportunities were more likely to be experiencing burnout (p = 0.038). Conclusions Trainees in radiology or pathology and those assigned to education at home were more likely to be concerned about their missed educational opportunities. Residency programs should consider providing trainees with research or at home clinical opportunities as an alternative to self-study should future need for reduced clinical hours arise.

scholarly journals Eye-blink artifact removal from single channel EEG with k-means and SSA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ajay Kumar Maddirala ◽  
Kalyana C Veluvolu
Keyword(s):  
Machine Learning ◽  
Single Channel ◽  
Learning Algorithm ◽  
Singular Spectrum Analysis ◽  
Machine Learning Algorithm ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Unsupervised Machine Learning ◽  
Eye Blink ◽  
Blink Artifact

AbstractIn recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ($$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

AGE-BASED VARIATIONS OF FRACTAL STRUCTURE OF EEG SIGNAL IN PATIENTS WITH EPILEPSY

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850051 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI
Keyword(s):  
Fractal Dimension ◽  
Brain Research ◽  
Age Groups ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals ◽  
Important Challenge ◽  
Patients With Epilepsy ◽  
Process Complexity ◽  
Electroencephalogram Eeg

It is known that aging affects neuroplasticity. On the other hand, neuroplasticity can be studied by analyzing the electroencephalogram (EEG) signal. An important challenge in brain research is to study the variations of neuroplasticity during aging for patients suffering from epilepsy. This study investigates the variations of the complexity of EEG signal during aging for patients with epilepsy. For this purpose, we employed fractal dimension as an indicator of process complexity. We classified the subjects in different age groups and computed the fractal dimension of their EEG signals. Our investigations showed that as patients get older, their EEG signal will be more complex. The method of investigation that has been used in this study can be further employed to study the variations of EEG signal in case of other brain disorders during aging.

DESIGN AND IMPLEMENTATION OF WIRELESS MULTI-CHANNEL EEG RECORDING SYSTEM AND STUDY OF EEG CLUSTERING METHOD

2006 ◽  
Vol 18 (06) ◽  
pp. 276-283 ◽  
Author(s):  
ROBERT LIN ◽  
REN-GUEY LEE ◽  
CHWAN-LU TSENG ◽  
YAN-FA WU ◽  
JOE-AIR JIANG
Keyword(s):  
Energy Operator ◽  
Digital Processing ◽  
Recording System ◽  
Ar Model ◽  
Characteristic Matrix ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Long Distance ◽  
Communication Module ◽  
Processing Circuit

A multi-channel wireless EEG (electroencephalogram) acquisition and recording system is developed in this work. The system includes an EEG sensing and transmission unit and a digital processing circuit. The former is composed of pre-amplifiers, filters, and gain amplifiers. The kernel of the later digital processing circuit is a micro-controller unit (MCU, TI-MSP430), which is utilized to convert the EEG signals into digital signals and fulfill the digital filtering. By means of Bluetooth communication module, the digitized signals are sent to the back-end such as PC or PDA. Thus, the patient's EEG signal can be observed and stored without any long cables such that the analogue distortion caused by long distance transmission can be reduced significantly. Furthermore, an integrated classification method, consisting of non-linear energy operator (NLEO), autoregressive (AR) model, and bisecting k-means algorithm, is also proposed to perform EEG off-line clustering at the back-end. First, the NLEO algorithm is utilized to divide the EEG signals into many small signal segments according to the features of the amplitude and frequency of EEG signals. The AR model is then applied to extract two characteristic values, i.e., frequency and amplitude (peak to peak value), of each segment and to form characteristic matrix for each segment of EEG signal. Finally, the improved modified k-means algorithm is utilized to assort similar EEG segments into better data classification, which allows accessing the long-term EEG signals more quickly.

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