scholarly journals Spectral Analysis of Electroencephalographic Data in Serious Games

2021 ◽  
Vol 11 (6) ◽  
pp. 2480
Author(s):  
Branko Babusiak ◽  
Marian Hostovecky ◽  
Maros Smondrk ◽  
Ladislav Huraj
Keyword(s):  
Serious Games ◽  
Brain Activity ◽  
Logic Gates ◽  
Logical Thinking ◽  
Mental Task ◽  
Basic Logic ◽  
Eeg Activity ◽  
Eeg Data ◽  
Processing Techniques ◽  
Signal Processing Techniques

In this paper, we describe an investigation of brain activity while playing a serious game (SG). A SG is focused on improving logical thinking, specifically on cognitive training of students in the field of basic logic gates, and we summarize SG description, design, and development. A method based on various signal processing techniques for evaluating electroencephalographic (EEG) data was implemented in the MATLAB. This assessment was based on the analysis of the spectrogram of particular brain activity. Changes in brain activity power at a characteristic frequency band during the gameplay were calculated from the spectrogram. The EEG of 21 respondents was measured. Based on the results, the respondents can be divided into three groups according to specific EEG activity changes during the gameplay compared to a relaxed state. The beta/alpha ratio, an indicator of brain employment to a mental task, was increased during gameplay in 18 of the 21 subjects. Our results reflected the sex of respondents, time of the game and the indicator, and whether the game was successfully completed.

scholarly journals Serious Games as Potential Therapies: A Validation Study of a Neurofeedback Game

2019 ◽  
Vol 51 (2) ◽  
pp. 87-93
Author(s):  
Femke Coenen ◽  
Floortje E. Scheepers ◽  
Saskia J. M. Palmen ◽  
Maretha V. de Jonge ◽  
Bob Oranje
Keyword(s):  
Health Care ◽  
Validation Study ◽  
Serious Games ◽  
Brain Activity ◽  
Healthy Male ◽  
Eeg Activity ◽  
Face To Face ◽  
Amplitude Data ◽  
The Brain ◽  
Concentration Condition

Serious (biofeedback) games offer promising ways to supplement or replace more expensive face-to-face interventions in health care. However, studies on the validity and effectiveness of EEG-based serious games remain scarce. In the current study, we investigated whether the conditions of the neurofeedback game “Daydream” indeed trained the brain activity as mentioned in the game manual. EEG activity was assessed in 14 healthy male volunteers while playing the 2 conditions of the game. The participants completed a training of 5 sessions. EEG frequency analyses were performed to verify the claims of the manual. We found significant differences in α- to β-ratio between the 2 conditions although only in the amplitude data, not in the power data. Within the conditions, mean α-amplitude only differed significantly from the β-amplitude in the concentration condition. Our analyses showed that neither α nor β brain activity differed significantly between game levels (higher level requiring increased brain activity) in either of the two conditions. In conclusion, we found only marginal evidence for the proposed claims stated in the manual of the game. Our research emphasizes that it is crucial to validate the claims that serious games make, especially before implementing them in the clinic or as therapeutic devices.

scholarly journals Brain-wide neural co-activations in resting human

2021 ◽  
Author(s):  
Lei Ding ◽  
Guofa Shou ◽  
Yoon-Hee Cha ◽  
John A. Sweeney ◽  
Han Yuan
Keyword(s):  
Resting State ◽  
Activation Patterns ◽  
Spontaneous Neural Activity ◽  
Co Activation ◽  
Eeg Data ◽  
Functional States ◽  
Processing Techniques ◽  
Human Brains ◽  
Signal Processing Techniques ◽  
Organizing Principles

AbstractSpontaneous neural activity in human as assessed with resting-state functional magnetic resonance imaging (fMRI) exhibits brain-wide coordinated patterns in the frequency of <0.1Hz. However, fast brain-wide networks at the timescales of neuronal events (milliseconds to sub-seconds) and their spatial, spectral, and propagational characteristics remain unclear due to the temporal constraints of hemodynamic signals. With milli-second resolution and whole-head coverage, scalp-based electroencephalography (EEG) provides a unique window into brain-wide networks with neuronal-timescale dynamics, shedding light on the organizing principles of brain function. Using state-of-the-art signal processing techniques, we reconstructed cortical neural tomography from resting-state EEG and extracted component-based co-activation patterns (cCAPs). These cCAPs revealed brain-wide intrinsic networks and their dynamics, indicating the configuration/reconfiguration of resting human brains into recurring and propagating functional states, which are featured with the prominent spatial phenomena of global patterns and anti-state pairs of co-(de)activations. Rich oscillational structures across a wide frequency band (i.e., 0.6Hz, 5Hz, and 10Hz) were embedded in the dynamics of these functional states. We further identified a superstructure that regulated between-state propagations and governed a significant aspect of brain-wide network dynamics. These findings demonstrated how resting-state EEG data can be functionally decomposed using cCAPs to reveal rich structures of brain-wide human neural activations.

scholarly journals SEREEGA: Simulating Event-Related EEG Activity

2018 ◽  
Author(s):  
Laurens R. Krol ◽  
Juliane Pawlitzki ◽  
Fabien Lotte ◽  
Klaus Gramann ◽  
Thorsten O. Zander
Keyword(s):  
Open Source ◽  
Brain Activity ◽  
Simulated Data ◽  
Ground Truth ◽  
List Type ◽  
Eeg Activity ◽  
Eeg Data ◽  
Different Types ◽  
Related Potentials ◽  
Eeg Recordings

AbstractElectroencephalography (EEG) is a popular method to monitor brain activity, but it can be difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings, ensuring that it is known beforehand which e ects are present in the data. As such, simulated data can be used, among other things, to assess or compare signal processing and machine learn-ing algorithms, to model EEG variabilities, and to design source reconstruction methods. In this paper, we present SEREEGA, short for Simulating Event-Related EEG Activity. SEREEGA is a MATLAB-based open-source toolbox dedicated to the generation of sim-ulated epochs of EEG data. It is modular and extensible, at initial release supporting ve different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general work ow of this toolbox, as well as a simulated data set demonstrating some of its functions.HighlightsSimulated EEG data has a known ground truth, which can be used to validate methods.We present a general-purpose open-source toolbox to simulate EEG data.It provides a single framework to simulate many different types of EEG recordings.It is modular, extensible, and already includes a number of head models and signals.It supports noise, oscillations, event-related potentials, connectivity, and more.

Nonlinear Signal Processing Techniques Applied to EEG Measurements

2011 ◽  
pp. 324-338
Author(s):  
Christos L. Papadelis ◽  
Chrysoula Koutidou-Papadeli ◽  
Panagiotis D. Bamidis ◽  
Nicos Maglaveras
Keyword(s):  
Brain Activity ◽  
Approximate Entropy ◽  
Fluctuation Analysis ◽  
Linear Measurements ◽  
Nonlinear Signal Processing ◽  
Hypoxic Conditions ◽  
Eeg Data ◽  
Nitrogen Mixture ◽  
Nonlinear Signal ◽  
Processing Techniques

The electrical activity of the brain is sensitive to its oxygen supply, and electroencephalography (EEG) has been proposed as a suitable measurement to detect brain activity alterations induced by hypoxia. Since, linear processing techniques that have been used so far in hypoxia studies are based on false linearity assumptions about the generation of the EEG signal, there is a definite need for nonlinear approaches to be applied on EEG data derived from hypoxic conditions. The aim of the present study is to compare nonlinear techniques’ effectiveness to identify significant variations in EEG due to hypoxia. EEG data from two channels were derived from ten healthy subjects participated in the present study. Oxygen and nitrogen mixture was used to simulate hypoxic conditions that correspond to an altitude of 25.000 feet. Non-linear measurements such as correlation dimension, approximate entropy, Lyapunov exponent and detrended fluctuation analysis (DFA) parameters were estimated for EEG signals. The results of the present study confirm the effectiveness of nonlinear techniques to identify significant variations in EEG, which reflect alterations in cerebral function induced by cerebral hypoxic conditions.

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

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