Clinical Diagnosis of Psychiatry Based on Electroencephalography

At present, neurophysiological signals used for neuro feedback are EEG (Electroencephalogram), functional magnetic resonance imaging. Among them, the acquisition of EEG signals has the advantages of non-invasive way with low cost. It has been widely used in brain-machine interface technology in recent years. Important progress has been made in rehabilitation and environmental control. However, neural feedback and brainmachine interface technology are completely similar in signal acquisition, signal feature extraction, and pattern classification. Therefore, the related research results of brain-machine interface can be used to closely cooperate with clinical needs to research and develop neural feedback technology based on EEG. Based on neurophysiology and brain-machine interface technology, this paper develops a neural feedback training system based on the acquisition and analysis of human EEG signals. Aiming at the autonomous rhythm components in the EEG signal, such as sensorimotor rhythm and alpha rhythm, the characteristic parameters are extracted through real-time EEG signal processing to generate feedback information, and the subject is self-regulated and trained from a physiological-psychological perspective by providing adjuvant treatment, a practical and stable treatment platform for the clinic.

Download Full-text

A portable real-time EEG signal acquisition and tele-medicine system

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-204582 ◽

2020 ◽

pp. 1-11

Author(s):

Qiang Zhang ◽

Peng Wang ◽

Shanshan Li ◽

Yonghao Jing

Keyword(s):

Real Time ◽

Sql Server ◽

Signal Acquisition ◽

Eeg Signal ◽

Eeg Signals ◽

Eeg Data ◽

Sql Server Database ◽

High Power Consumption ◽

Machine Interface ◽

Sd Card

Since electroencephalogram (EEG) signals contain a variety of physiological and pathological information, they are widely used in medical diagnosis, brain machine interface and other fields. The existing EEG apparatus are not perfect due to big size, high power consumption and using cables to transmit data. In this paper, a portable real-time EEG signal acquisition and tele-medicine system is developed in order to improve performance of EEG apparatus. The weak EEG signals are induced to the pre-processing circuits via a noninvasive method with bipolar leads. After multi-level amplifying and filtering, these signals are transmitted to DSP (TMS320C5509) to conduct digital filtering. Then, the EEG signals are displayed on the LCD screen and stored in the SD card so that they can be uploaded to the server through the internet. The server employs SQL Server database to manage patients’ information and to store data in disk. Doctors can download, look up and analyze patients’ EEG data using the doctor client. Experimental results demonstrate that the system can acquire weak EEG signals in real time, display the processed results, save data and carry out tele-medicine. The system can meet the requirement of the EEG signals’ quality, and are easy to use and carry.

Download Full-text

Exploring Neurofeedback Training for BMI Power Augmentation of Upper Limbs: A Pilot Study

Entropy ◽

10.3390/e23040443 ◽

2021 ◽

Vol 23 (4) ◽

pp. 443

Author(s):

Hongbo Liang ◽

Shota Maedono ◽

Yingxin Yu ◽

Chang Liu ◽

Naoya Ueda ◽

...

Keyword(s):

Training Session ◽

Training System ◽

Eeg Signals ◽

Shoulder Joints ◽

Power Spectral ◽

Training Stage ◽

Beta Power ◽

Eeg Changes ◽

Machine Interface ◽

Flexion And Extension

Electroencephalography neurofeedback (EEG-NFB) training can induce changes in the power of targeted EEG bands. The objective of this study is to enhance and evaluate the specific changes of EEG power spectral density that the brain-machine interface (BMI) users can reliably generate for power augmentation through EEG-NFB training. First, we constructed an EEG-NFB training system for power augmentation. Then, three subjects were assigned to three NFB training stages, based on a 6-day consecutive training session as one stage. The subjects received real-time feedback from their EEG signals by a robotic arm while conducting flexion and extension movement with their elbow and shoulder joints, respectively. EEG signals were compared with each NFB training stage. The training results showed that EEG beta (12–40 Hz) power increased after the NFB training for both the elbow and the shoulder joints’ movements. EEG beta power showed sustained improvements during the 3-stage training, which revealed that even the short-term training could improve EEG signals significantly. Moreover, the training effect of the shoulder joints was more obvious than that of the elbow joints. These results suggest that NFB training can improve EEG signals and clarify the specific EEG changes during the movement. Our results may even provide insights into how the neural effects of NFB can be better applied to the BMI power augmentation system and improve the performance of healthy individuals.

Download Full-text

Design and Verification of a Dry Sensor-Based Multi-Channel Digital Active Circuit for Human Brain Electroencephalography Signal Acquisition Systems

Micromachines ◽

10.3390/mi10110720 ◽

2019 ◽

Vol 10 (11) ◽

pp. 720

Author(s):

Chin-Teng Lin ◽

Chi-Hsien Liu ◽

Po-Sheng Wang ◽

Jung-Tai King ◽

Lun-De Liao

Keyword(s):

Daily Life ◽

Event Related Potentials ◽

Computer Interface ◽

Signal Acquisition ◽

Eeg Signal ◽

Eeg Signals ◽

Preparation Time ◽

Active Circuit ◽

Related Potentials ◽

Common Application

A brain–computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled individuals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system.

Download Full-text

A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface

IEEE Transactions on Biomedical Circuits and Systems ◽

10.1109/tbcas.2016.2574362 ◽

2016 ◽

Vol 10 (4) ◽

pp. 874-883 ◽

Cited By ~ 56

Author(s):

Xilin Liu ◽

Milin Zhang ◽

Tao Xiong ◽

Andrew G. Richardson ◽

Timothy H. Lucas ◽

...

Keyword(s):

Compressed Sensing ◽

Acquisition System ◽

Brain Machine Interface ◽

Signal Acquisition ◽

Neural Signal ◽

Fully Integrated ◽

Chronic Recording ◽

Machine Interface

Download Full-text

Robust, ultra low-cost MMG system with brain-machine-interface applications

2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) ◽

10.1109/ner.2013.6696036 ◽

2013 ◽

Cited By ~ 14

Author(s):

Salvatore Fara ◽

Chandra Sen Vikram ◽

Constantinos Gavriel ◽

A. Aldo Faisal

Keyword(s):

Low Cost ◽

Brain Machine Interface ◽

Machine Interface

Download Full-text

Coherence analysis of EEG in locomotion using graphs

10.17488/rmib.30.1.18 ◽

2017 ◽

Author(s):

◽

G. Quiroz

Keyword(s):

Assistive Devices ◽

Brain Machine Interface ◽

Coherence Analysis ◽

Connectivity Analysis ◽

Eeg Signals ◽

Time Frequency ◽

Temporal Features ◽

Machine Interface ◽

Spatio Temporal ◽

Motion Intention

One of the most interesting brain machine interface (BMI) applications, is the control of assistive devices for rehabilitation of neuromotor pathologies. This means that assistive devices (prostheses, orthoses, or exoskeletons) are able to detect user motion intention, by the acquisition and interpretation of electroencephalographic (EEG) signals. Such interpretation is based on the time, frequency or space features of the EEG signals. For this reason, in this paper a coherence-based EEG study is proposed during locomotion that along with the graph theory allows to establish spatio-temporal parameters that are characteristic in this study. The results show that along with the temporal features of the signal it is possible to find spatial patterns in order to classify motion tasks of interest. In this manner, the connectivity analysis alongside graphs provides reliable information about the spatio-temporal characteristics of the neural activity, showing a dynamic pattern in the connectivity during locomotions tasks.

Download Full-text

Application of music in relief of driving fatigue based on EEG signals

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-021-00794-8 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Qingjun Wang ◽

Zhendong Mu

Keyword(s):

Traffic Accidents ◽

Signal Acquisition ◽

Eeg Signal ◽

Eeg Signals ◽

Driving Time ◽

Mode Decomposition ◽

Depth Analysis ◽

Driving Fatigue ◽

Highly Correlated ◽

Fatigue Driving

AbstractIn order to solve the problem of traffic accidents caused by fatigue driving, the research of EEG signals is particularly important, which can timely and accurately determine the fatigue state and take corresponding measures. Effective fatigue improvement measures are an important research topic in the current scientific field. The purpose of this article is to use EEG signals to analyze fatigue driving and prevent the dangers and injuries caused by fatigue driving. We designed the electroencephalogram (EEG) signal acquisition model to collect the EEG signal of the experimenter, and then removed the noise through the algorithm of Variational Mode Decomposition (VMD) and independent component analysis (ICA). On the basis of in-depth analysis and full understanding, we learned about the EEG signal of the driver at different driving times and different landscape roads, and provided some references for the study of music in relieving driving fatigue. The results of the study show that in the presence of music, the driver can keep the EEG signal active for more than 2 h, while in the absence of music, the driver’s EEG signal is active for about 1.5 h. Under different road conditions, the driver’s EEG signal activity is not consistent. The β wave and (α + θ)/β ratio of the driver in mountainous roads and grassland road landscape environments are highly correlated with driving time, and β wave is negatively correlated with driving time, and (α + θ)/β is positively correlated with driving time. In addition, the accumulation of changes in the two indicators is also strongly correlated with driving time.

Download Full-text

Implementation of Neural Network with ALE for the Removal of Artifacts in EEG Signals

Current Signal Transduction Therapy ◽

10.2174/1574362414666190613142424 ◽

2020 ◽

Vol 15 (1) ◽

pp. 77-83

Author(s):

R. Suresh Kumar ◽

P. Manimegalai

Keyword(s):

Neural Network ◽

Low Cost ◽

Information Source ◽

Activation Function ◽

Signal Extraction ◽

Eeg Signal ◽

Least Mean Square ◽

Eeg Signals ◽

Adaptive Line Enhancer ◽

Field Programmable

Objective: The EEG signal extraction offers an opportunity to improve the quality of life in patients, which has lost to control the ability of their body, with impairment of locomotion. Electroencephalogram (EEG) signal is an important information source for underlying brain processes. Materials and Methods: The signal extraction and denoising technique obtained through timedomain was then processed by Adaptive Line Enhancer (ALE) to extract the signal coefficient and classify the EEG signals based on FF network. The adaptive line enhancer is used to update the coefficient during the runtime with the help of adaptive algorithms (LMS, RLS, Kalman Filter). Results: In this work, the least mean square algorithm was employed to obtain the coefficient update with respect to the corresponding input signal. Finally, Mat lab and verilog HDL language are used to simulate the signals and got the classification accuracy rate of 80%. Conclusion: Experiments show that this method can get high and accurate rate of classification. In this paper, it is proposed that a low-cost use of Field Programmable Gate Arrays (FPGAs) can be used to process EEG signals for extracting and denoising. As a preliminary study, this work shows the implementation of a Neural Network, integrated with ALE for EEG signal processing. The preliminary tests through the proposed architecture for the activation function shows to be reasonable both in terms of precision and in processing speed.

Download Full-text