scholarly journals Decode Brain System: A Dynamic Adaptive Convolutional Quorum Voting Approach for Variable-Length EEG Data

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Tao Xu ◽  
Yun Zhou ◽  
Zekai Hou ◽  
Wenlan Zhang
Keyword(s):  
Time Window ◽  
Brain Activity ◽  
Sliding Window ◽  
Variable Length ◽  
Cognitive State ◽  
Human Beings ◽  
Processing Variable ◽  
Cognitive States ◽  
Eeg Data ◽  
Eeg Recordings

The brain is a complex and dynamic system, consisting of interacting sets and the temporal evolution of these sets. Electroencephalogram (EEG) recordings of brain activity play a vital role to decode the cognitive process of human beings in learning research and application areas. In the real world, people react to stimuli differently, and the duration of brain activities varies between individuals. Therefore, the length of EEG recordings in trials gathered in the experiment is variable. However, current approaches either fix the length of EEG recordings in each trial which would lose information hidden in the data or use the sliding window which would consume large computation on overlapped parts of slices. In this paper, we propose TOO (Traverse Only Once), a new approach for processing variable-length EEG trial data. TOO is a convolutional quorum voting approach that breaks the fixed structure of the model through convolutional implementation of sliding windows and the replacement of the fully connected layer by the 1 × 1 convolutional layer. Each output cell generated from 1 × 1 convolutional layer corresponds to each slice created by a sliding time window, which reflects changes in cognitive states. Then, TOO employs quorum voting on output cells and determines the cognitive state representing the entire single trial. Our approach provides an adaptive model for trials of different lengths with traversing EEG data of each trial only once to recognize cognitive states. We design and implement a cognitive experiment and obtain EEG data. Using the data collecting from this experiment, we conducted an evaluation to compare TOO with a state-of-art sliding window end-to-end approach. The results show that TOO yields a good accuracy (83.58%) at the trial level with a much lower computation (11.16%). It also has the potential to be used in variable signal processing in other application areas.

Aligning Brain Activity and Sketch in Multi-Modal CAD Interface

2014 ◽  
Author(s):  
Somayeh B. Shafiei ◽  
Ehsan Tarkesh Esfahani
Keyword(s):  
Time Window ◽  
Mental Workload ◽  
Brain Activity ◽  
Time Lag ◽  
Cognitive State ◽  
Eeg Signals ◽  
Scalp Eeg ◽  
Cognitive States ◽  
Series Of Experiments ◽  
Dynamic Time

This paper investigates the proper synchronization of sketch data and cognitive states in a multi-modal CAD interface. In a series of experiments, 5 subjects were instructed to watch and then explain 6 mechanical mechanisms by sketching them on a touch based screen. Simultaneously, subject’s brain waves were recorded in terms of electroencephalogram (EEG) signals from 9 locations on the scalp. EEG signals were analyzed and translated into mental workload and cognitive state. A dynamic time window was then constructed to align these features with sketch features such that the combination of two modalities maximizes the classification of gesture from non-gesture strokes. Quadratic Discriminant Analysis (QDA) was used as classification method. Our experimental results show that the best temporal alignment for workload and sketch analysis starts from 30% time lag with previous stroke and ends before 30% time lag with next stroke.

scholarly journals Signal-space projection suppresses the tACS artifact in EEG recordings

2019 ◽  
Author(s):  
Johannes Vosskuhl ◽  
Tuomas P. Mutanen ◽  
Toralf Neuling ◽  
Risto J. Ilmoniemi ◽  
Christoph S. Herrmann
Keyword(s):  
Brain Activity ◽  
Current Source ◽  
Oscillatory Activity ◽  
Signal Space ◽  
Artifact Correction ◽  
Eeg Data ◽  
Before And After ◽  
Transcranial Alternating Current Stimulation ◽  
Space Projection ◽  
Eeg Recordings

1.AbstractBackgroundTo probe the functional role of brain oscillations, transcranial alternating current stimulation (tACS) has proven to be a useful neuroscientific tool. Because of the huge tACS-caused artifact in electroencephalography (EEG) signals, tACS–EEG studies have been mostly limited to compare brain activity between recordings before and after concurrent tACS. Critically, attempts to suppress the artifact in the data cannot assure that the entire artifact is removed while brain activity is preserved. The current study aims to evaluate the feasibility of specific artifact correction techniques to clean tACS-contaminated EEG data.New MethodIn the first experiment, we used a phantom head to have full control over the signal to be analyzed. Driving pre-recorded human brain-oscillation signals through a dipolar current source within the phantom, we simultaneously applied tACS and compared the performance of different artifact-correction techniques: sine subtraction, template subtraction, and signal-space projection (SSP). In the second experiment, we combined tACS and EEG on a human subject to validate the best-performing data-correction approach.ResultsThe tACS artifact was highly attenuated by SSP in the phantom and the human EEG; thus, we were able to recover the amplitude and phase of the oscillatory activity. In the human experiment, event-related desynchronization could be restored after correcting the artifact.Comparison with existing methodsThe best results were achieved with SSP, which outperformed sine subtraction and template subtraction.ConclusionsOur results demonstrate the feasibility of SSP by applying it to human tACS–EEG data.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2019 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2020 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

scholarly journals Signal-Space Projection Suppresses the tACS Artifact in EEG Recordings

2020 ◽  
Vol 14 ◽  
Author(s):  
Johannes Vosskuhl ◽  
Tuomas P. Mutanen ◽  
Toralf Neuling ◽  
Risto J. Ilmoniemi ◽  
Christoph S. Herrmann
Keyword(s):  
Brain Activity ◽  
Current Source ◽  
Oscillatory Activity ◽  
Signal Space ◽  
Artifact Correction ◽  
Eeg Data ◽  
Before And After ◽  
Transcranial Alternating Current Stimulation ◽  
Space Projection ◽  
Eeg Recordings

BackgroundTo probe the functional role of brain oscillations, transcranial alternating current stimulation (tACS) has proven to be a useful neuroscientific tool. Because of the excessive tACS-caused artifact at the stimulation frequency in electroencephalography (EEG) signals, tACS + EEG studies have been mostly limited to compare brain activity between recordings before and after concurrent tACS. Critically, attempts to suppress the artifact in the data cannot assure that the entire artifact is removed while brain activity is preserved. The current study aims to evaluate the feasibility of specific artifact correction techniques to clean tACS-contaminated EEG data.New MethodIn the first experiment, we used a phantom head to have full control over the signal to be analyzed. Driving pre-recorded human brain-oscillation signals through a dipolar current source within the phantom, we simultaneously applied tACS and compared the performance of different artifact-correction techniques: sine subtraction, template subtraction, and signal-space projection (SSP). In the second experiment, we combined tACS and EEG on one human subject to demonstrate the best-performing data-correction approach in a proof of principle.ResultsThe tACS artifact was highly attenuated by SSP in the phantom and the human EEG; thus, we were able to recover the amplitude and phase of the oscillatory activity. In the human experiment, event-related desynchronization could be restored after correcting the artifact.Comparison With Existing MethodsThe best results were achieved with SSP, which outperformed sine subtraction and template subtraction.ConclusionOur results demonstrate the feasibility of SSP by applying it to a phantom measurement with pre-recorded signal and one human tACS + EEG dataset. For a full validation of SSP, more data are needed.

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.

scholarly journals Oscillation-based connectivity architecture is dominated by an intrinsic spatial organization, not cognitive state or frequency

2020 ◽  
Author(s):  
Parham Mostame ◽  
Sepideh Sadaghiani
Keyword(s):  
Information Exchange ◽  
Spatial Organization ◽  
Brain Activity ◽  
The State ◽  
Neural Oscillations ◽  
Cognitive State ◽  
State Dependency ◽  
Frequency Bands ◽  
Cognitive States ◽  
The Impact

AbstractFunctional connectivity (FC) of neural oscillations (~1-150Hz) is thought to facilitate neural information exchange across brain areas by forming malleable neural ensembles in the service of cognitive processes. However, neural oscillations and their FC are not restricted to certain cognitive demands and continuously unfold in all cognitive states. To what degree is the spatial organization of oscillation-based FC affected by cognitive state or governed by an intrinsic architecture? And what is the impact of oscillation frequency and FC mode (phase-versus amplitude coupling)? Using ECoG recordings of 18 presurgical patients, we quantified the state-dependency of oscillation-based FC in five canonical frequency bands and across an array of 6 task states. For both phase- and amplitude coupling, static FC analysis revealed a spatially largely state-invariant (i.e. intrinsic) component in all frequency bands. Further, the observed intrinsic FC pattern was spatially similar across all frequency bands. However, temporally independent FC dynamics in each frequency band allow for frequency-specific malleability in information exchange. In conclusion, the spatial organization of oscillation-based FC is largely stable over cognitive states, i.e. primarily intrinsic in nature, and shared across frequency bands. The state-invariance is in line with prior findings at the other temporal extreme of brain activity, the infraslow range (~<0.1Hz) observed in fMRI. Our observations have implications for conceptual frameworks of oscillation-based FC and the analysis of task-related FC changes.

Cognitive State Estimation by Effective Feature Extraction and Proper Channel Selection of EEG Signal

2014 ◽  
Vol 24 (02) ◽  
pp. 1540005 ◽  
Author(s):  
Monira Islam ◽  
Tazrin Ahmed ◽  
Md. Salah Uddin Yusuf ◽  
Mohiuddin Ahmad
Keyword(s):  
Feature Extraction ◽  
Cognitive State ◽  
Eeg Signal ◽  
Classification Rate ◽  
Time Frequency ◽  
Cognitive States ◽  
Alpha Channel ◽  
Power Spectral ◽  
Eeg Data ◽  
Selection Of

This paper presents a cognitive state estimation system focused on some effective feature extraction based on temporal and spectral analysis of electroencephalogram (EEG) signal and the proper channel selection of the BIOPAC automated EEG analysis system. In the proposed approach, different frequency components (i) real value; (ii) imaginary value; (iii) magnitude; (iv) phase angle and (v) power spectral density of the EEG data samples during different mental task performed to assess seven types of human cognitive states — relax, mental task, memory related task, motor action, pleasant, fear and enjoying music on the three channels of BIOPAC EEG data acquisition system — EEG, Alpha and Alpha RMS signal. Also the time and time-frequency-based features were extracted to compare the performance of the system. After feature extraction, the channel efficacy is evaluated by support vector machine (SVM) based on the classification rate in different cognitive states. From the experimental results and classification accuracy, it is determined that the overall accuracy for alpha channel shows much improved result for power spectral density than the other frequency based features and other channels. The classification rate is 69.17% for alpha channel whereas for EEG and alpha RMS channel it is found 47.22% and 32.21%, respectively. For statistical analysis standard deviation shows better result for alpha channel and it is found 65.4%. The time-frequency analysis shows much improved result for alpha channel also. For the mean value of DWT coefficients the accuracy is highest and it is 81.3%. Besides the classification accuracy, SVM shows better performance in compare with kNN classifier.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2021 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

scholarly journals EEG data processing with neural network

2019 ◽  
Vol 12 (2) ◽  
pp. 33-36 ◽  
Author(s):  
Tamás Majoros ◽  
Balázs Ujvári ◽  
Stefan Oniga
Keyword(s):  
Neural Network ◽  
High Efficiency ◽  
Brain Activity ◽  
Feedforward Neural Network ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Eeg Data ◽  
Measurement Results ◽  
Eeg Recordings ◽  
Extract Information

Abstract Machine-learning techniques allow to extract information from electroencephalographic (EEG) recordings of brain activity. By processing the measurement results of a publicly available EEG dataset, we were able to obtain information that could be used to train a feedforward neural network to classify two types of volunteer activities with high efficiency.

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