Fast DCT algorithms for EEG data compression in embedded systems

Electroencephalography (EEG) is widely used in clinical diagnosis, monitoring and Brain - Computer Interface systems. Usually EEG signals are recorded with several electrodes and transmitted through a communication channel for further processing. In order to decrease communication bandwidth and transmission time in portable or low cost devices, data compression is required. In this paper we consider the use of fast Discrete Cosine Transform (DCT) algorithms for lossy EEG data compression. Using this approach, the signal is partitioned into a set of 8 samples and each set is DCT-transformed. The least-significant transform coefficients are removed before transmission and are filled with zeros before an inverse transform. We conclude that this method can be used in real-time embedded systems, where low computational complexity and high speed is required.

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

ACM Transactions on Management Information Systems ◽

10.1145/3458791 ◽

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.

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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 ◽

10.3390/s20040988 ◽

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.

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Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2011.3.2126 ◽

2011 ◽

Vol 6 (3) ◽

pp. 403 ◽

Cited By ~ 1

Author(s):

Ioan Dzitac ◽

Tiberiu Vesselényi ◽

Radu Cătălin Ţarcă

Keyword(s):

Fuzzy Inference ◽

Brain Activity ◽

Brain Computer Interface ◽

Computer Interface ◽

Eeg Signals ◽

Inference System ◽

Eeg Data ◽

Recent Developments ◽

Potential Activity ◽

Inference Systems

A Brain-Computer Interface uses measurements of scalp electric potential (electroencephalography - EEG) reflecting brain activity, to communicate with external devices. Recent developments in electronics and computer sciences have enabled applications that may help users with disabilities and also to develop new types of Human Machine Interfaces. By producing modifications in their brain potential activity, the users can perform control of different devices. In order to perform actions, this EEG signals must be processed with proper algorithms. Our approach is based on a fuzzy inference system used to produce sharp control states from noisy EEG data.

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Low-Cost Brain-Computer Interface Using the Emotiv Epoc Headset Based on Rotating Vanes

Traitement du signal ◽

10.18280/ts.370516 ◽

2020 ◽

Vol 37 (5) ◽

pp. 831-837

Author(s):

Mesut Melek ◽

Negin Manshouri ◽

Temel Kayikcioglu

Keyword(s):

Information Transfer ◽

Brain Computer Interface ◽

Low Cost ◽

Computer Interface ◽

Digital Signals ◽

Eeg Signals ◽

The Gaze ◽

Information Transfer Rate ◽

The Brain ◽

Bci System

Detailed In the brain-computer interface system (BCI), electroencephalography (EEG) signals are converted into digital signals and analyzed, allowing direct communication between humans and the electronic devices around them. The convenience of the user and the speed of communication with the surrounding devices are the most important challenges of BCI systems. The Emotiv Epoc headset minimizes the discomfort of the user thanks to its wet electrodes and easy handling. In the continuation of our previous works, in this paper, we developed our BCI system based on the gaze at the rotating vanes using the inexpensive Emotiv Epoc headset. In addition to user comfort, our design has an acceptable mean accuracy rate (ACC) and mean information transfer rate (ITR) compared to similar systems.

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Потоковая обработка многоканальных сигналов электроэнцефалограммы с использованием технологии параллельных вычислений на графических процессорах NVIDIA CUDA

Письма в журнал технической физики ◽

10.21883/pjtf.2018.10.46105.17219 ◽

2018 ◽

Vol 44 (10) ◽

pp. 103

Author(s):

В.В. Грубов ◽

В.О. Недайвозов

Keyword(s):

Neural Activity ◽

Brain Computer Interface ◽

Spectral Characteristics ◽

Cognitive Activity ◽

Computer Interface ◽

Computing Technology ◽

Eeg Signals ◽

Nvidia Cuda ◽

Eeg Data

AbstractProspects of using parallel computing technology (PaCT) methods for the stream processing and online analysis of multichannel EEG data are considered. It is shown that the application of PaCT to calculation and evaluation of spectral characteristics of EEG signals makes online determination of changes in the energy of the main rhythms of neural activity in various parts of the cerebral cortex possible. The possibility of implementing the PaCT algorithm with CUDA C library and its use in a modern brain–computer interface (BCI) for cognitive-activity monitoring in the course of visual perception.

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Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2011.3.2125 ◽

2011 ◽

Vol 6 (3) ◽

pp. 403 ◽

Cited By ~ 6

Author(s):

Ioan Dzitac ◽

Tiberiu Vesselényi ◽

Radu Cătălin Ţarcă

Keyword(s):

Fuzzy Inference ◽

Brain Activity ◽

Brain Computer Interface ◽

Computer Interface ◽

Eeg Signals ◽

Inference System ◽

Eeg Data ◽

Recent Developments ◽

Potential Activity ◽

Inference Systems

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NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

International Conference on Technics Technologies and Education ◽

10.15547/ictte.2019.02.095 ◽

2019 ◽

pp. 139-145

Author(s):

Selma Büyükgöze

Keyword(s):

Brain Computer Interface ◽

Computer Interface ◽

Electrode Placement ◽

Eeg Signals ◽

Non Invasive ◽

Brain Signals ◽

Brain States ◽

The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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