scholarly journals Control of electric wheelchair by eye activities using eeg technique

2013 ◽  
Vol 16 (4) ◽  
pp. 18-28
Author(s):  
Hai Thanh Nguyen ◽  
Toi Van Vo ◽  
Trung Van Nguyen
Keyword(s):  
Original Data ◽  
Band Pass Filter ◽  
Threshold Method ◽  
Pass Filter ◽  
Delta Area ◽  
Electric Wheelchair ◽  
Eeg Data ◽  
Band Pass ◽  
Bci System ◽  
Threshold Approach

This paper presents a study control of an electric wheelchair based on ElectroEncephaloGraphy (EEG). The directions of wheelchair are controlled by eye activities. A mean threshold algorithm is proposed to detect eye activities using EEG technique. The activities of eyes such as blinking two eyes, glanced left and glanced right related to the delta area of human brain are investigated. Before analyzing the EEG data, original data are filtered to reduce noise or artifacts by a band-pass filter. The proposed threshold method is applied to distinguish the phenomenon of eye activities. This study is useful for creating a BCI system such as wheelchair control. Experimental results show that the proposed threshold approach is the effectiveness.

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Damodar Reddy Edla ◽  
Shubham Dodia ◽  
Annushree Bablani ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Brain Computer Interface ◽  
Wavelet Packet ◽  
Stimulus Onset ◽  
Computer Interface ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Pass Filter ◽  
Eeg Data ◽  
Identification Test ◽  
Band Pass

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

A Portable Electroencephalogram Recording System for Rats

2014 ◽  
Vol 577 ◽  
pp. 1236-1240
Author(s):  
Dian Zhang ◽  
Bo Wang ◽  
Qing Liang Qin
Keyword(s):  
Recording System ◽  
Band Pass Filter ◽  
Test Results ◽  
Electronic Components ◽  
Eeg Signals ◽  
Pass Filter ◽  
Wireless Transmitter ◽  
Eeg Data ◽  
Band Pass ◽  
Electroencephalogram Eeg

A wireless portable electroencephalogram (EEG) recording system for animals was designed, manufactured and then tested in rats. The system basically consisted of four modules: 1) EEG collecting module with the wireless transmitter and receiver (designed by NRF24LE1), 2) filter bank consisting of pre-amplifier, band pass filter and 50Hz trapper, 3) power management module and 4) display interface for showing EEG signals. The EEG data were modulated firstly and emitted by the wireless transmitter after being amplified and filtered. The receiver demodulated and displayed the signals in voltage through serial port. The system was designed as surface mount devices (SMD) with small size (20mm×25mm×3mm) and light weight (4g), and was fabricated of electronic components that were commercially available. The test results indicated that in given environment the system could stably record more than 8 hours and transmit EEG signals over a distance of 20m. Our system showed the features of small size, low power consumption and high accuracy which were suitable for EEG telemetry in rats.

OBSERVATION AND SIMULATION OF TSUNAMIS INDUCED BY THE 2003 TOKACHI-OKI EARTHQUAKE (M 8.0)

2015 ◽  
Vol 2 (3) ◽  
Author(s):  
Tatsuo Ohmachi ◽  
Shusaku Inoue ◽  
Tetsuji Imai
Keyword(s):  
Rayleigh Wave ◽  
Water Pressure ◽  
Near Field ◽  
Source Region ◽  
Tsunami Source ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Tsunami Simulation ◽  
Sea Bed ◽  
Band Pass

The 2003 Tokachi-oki earthquake (MJ 8.0) occurred off the southeastern coast of Tokachi, Japan, and generated a large tsunami which arrived at Tokachi Harbor at 04:56 with a wave height of 4.3 m. Japan Marine Science and Technology Center (JAMSTEC) recovered records of water pressure and sea-bed acceleration at the bottom of the tsunami source region. These records are first introduced with some findings from Fourier analysis and band-pass filter analysis. Water pressure disturbance lasted for over 30 minutes and the duration was longer than those of accelerations. Predominant periods of the pressure looked like those excited by Rayleigh waves. Next, numerical simulation was conducted using the dynamic tsunami simulation technique able to represent generation and propagation of Rayleigh wave and tsunami, with a satisfactory result showing validity and usefulness of this technique. Keywords: Earthquake, Rayleigh wave, tsunami, near-field

A comparative study on the demodulation capability of a single loop optoelectronic oscillator with and without a dynamic band Pass filter

Optik ◽  
2021 ◽  
Vol 226 ◽  
pp. 165924
Author(s):  
Shantanu Mandal ◽  
Kousik Bishayee ◽  
Arindum Mukherjee ◽  
B N Biswas ◽  
Chandan Kumar Sarkar
Keyword(s):  
Comparative Study ◽  
Band Pass Filter ◽  
Optoelectronic Oscillator ◽  
Pass Filter ◽  
Single Loop ◽  
Band Pass
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