scholarly journals Regularized two-step brain activity reconstruction from spatiotemporal EEG data

2004 ◽  
Author(s):  
Teodor I. Alecu ◽  
Sviatoslav Voloshynovskiy ◽  
Thierry Pun
Keyword(s):  
Brain Activity ◽  
Eeg Data

scholarly journals ConvDip: A convolutional neural network for better M/EEG Source Imaging

2020 ◽  
Author(s):  
Lukas Hecker ◽  
Rebekka Rupprecht ◽  
Ludger Tebartz van Elst ◽  
Juergen Kornmeier
Keyword(s):  
Neural Network ◽  
Inverse Problem ◽  
Convolutional Neural Network ◽  
Brain Activity ◽  
Clinical Diagnostics ◽  
Dipole Model ◽  
Source Imaging ◽  
Eeg Source Imaging ◽  
Eeg Data ◽  
Inverse Solutions

AbstractEEG and MEG are well-established non-invasive methods in neuroscientific research and clinical diagnostics. Both methods provide a high temporal but low spatial resolution of brain activity. In order to gain insight about the spatial dynamics of the M/EEG one has to solve the inverse problem, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipoles sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods (eLORETA and LCMV beamforming) on all focused performance measures. (3) It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. (4) It produces plausible inverse solutions for real-world EEG recordings and needs less than 40 ms for a single forward pass. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG and MEG data, with high relevance for clinical applications, e.g. in epileptology and real time applications.

scholarly journals CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

2020 ◽  
Author(s):  
Diego Fabian Collazos Huertas ◽  
Andres Marino Alvarez Meza ◽  
German Castellanos Dominguez
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Continuous Wavelet Transform ◽  
Brain Activity ◽  
Machine Learning Techniques ◽  
Continuous Wavelet ◽  
Brain Response ◽  
Power Spectral ◽  
Learning Techniques ◽  
Eeg Data

Abstract Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.

scholarly journals BSoniq: A 3-D EEG Sound Installation

2017 ◽  
Author(s):  
Marlene Mathew ◽  
Mert Cetinkaya ◽  
Agnieszka Roginska
Keyword(s):  
Brain Activity ◽  
Spatial Location ◽  
Data Access ◽  
Mental States ◽  
Brain Wave ◽  
Sound Installation ◽  
Eeg Data ◽  
Additional Process ◽  
Event Based ◽  
Exciting Possibility

Brain Computer Interface (BCI) methods have received a lot of attention in the past several decades, owing to the exciting possibility of computer-aided communication with the outside world. Most BCIs allow users to control an external entity such as games, prosthetics, musical output etc. or are used for offline medical diagnosis processing. Most BCIs that provide neurofeedback, usually categorize the brainwaves into mental states for the user to interact with. Raw brainwave interaction by the user is not usually a feature that is readily available for a lot of popular BCIs. If there is, the user has to pay for or go through an additional process for raw brain wave data access and interaction. BSoniq is a multi-channel interactive neurofeedback installation which, allows for real-time sonification and visualization of electroencephalogram (EEG) data. This EEG data provides multivariate information about human brain activity. Here, a multivariate event-based sonification is proposed using 3D spatial location to provide cues about these particular events. With BSoniq, users can listen to the various sounds (raw brain waves) emitted from their brain or parts of their brain and perceive their own brainwave activities in a 3D spatialized surrounding giving them a sense that they are inside their own heads.

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.

Classification Algorithms for EEG-Based Brain-Computer Interface

2019 ◽  
pp. 52-73
Author(s):  
Sravanth Kumar Ramakuri ◽  
Chinmay Chakraboirty ◽  
Anudeep Peddi ◽  
Bharat Gupta
Keyword(s):  
Brain Activity ◽  
Biomedical Signal Processing ◽  
Computer Interface ◽  
Eeg Signals ◽  
Large Sets ◽  
Eeg Data ◽  
Important Measurement ◽  
The Brain

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

scholarly journals Comparison of EEG Data Classification between Conventional Visual Cue-Marker and EMG-Based Marker on Brain Activity

2018 ◽  
Vol 24 ◽  
pp. 66-73
Author(s):  
Khairunnisa Johar ◽  
Noor Ayuni Che Zakaria ◽  
Muhammad Azmi Ayub ◽  
Cheng Yee Low ◽  
Fazah Akthar Hanapiah
Keyword(s):  
Brain Activity ◽  
Data Classification ◽  
Visual Cue ◽  
Eeg Data

scholarly journals Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test

Computers ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 67
Author(s):  
Vasco Ponciano ◽  
Ivan Miguel Pires ◽  
Fernando Reinaldo Ribeiro ◽  
María Vanessa Villasana ◽  
Maria Canavarro Teixeira ◽  
...  
Keyword(s):  
Heart Rate ◽  
Experimental Study ◽  
Heart Rate Variability ◽  
Mobile Devices ◽  
Brain Activity ◽  
Health Conditions ◽  
Qrs Complex ◽  
Timed Up And Go ◽  
Eeg Data ◽  
Ecg Data

The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual’s heartbeat. Similarly, by using the EEG sensor, one could analyze the individual’s brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly’s experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, linear heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.

Reconstruction of Neural Activity from EEG Data Using Dynamic Spatiotemporal Constraints

2016 ◽  
Vol 26 (07) ◽  
pp. 1650026 ◽  
Author(s):  
E. Giraldo-Suarez ◽  
J. D. Martinez-Vargas ◽  
G. Castellanos-Dominguez
Keyword(s):  
Spatial Resolution ◽  
Neural Activity ◽  
Focal Epilepsy ◽  
Experimental Testing ◽  
Brain Activity ◽  
Brain Regions ◽  
Basis Set ◽  
Eeg Signals ◽  
Reconstruction Performance ◽  
Eeg Data

We present a novel iterative regularized algorithm (IRA) for neural activity reconstruction that explicitly includes spatiotemporal constraints, performing a trade-off between space and time resolutions. For improving the spatial accuracy provided by electroencephalography (EEG) signals, we explore a basis set that describes the smooth, localized areas of potentially active brain regions. In turn, we enhance the time resolution by adding the Markovian assumption for brain activity estimation at each time period. Moreover, to deal with applications that have either distributed or localized neural activity, the spatiotemporal constraints are expressed through [Formula: see text] and [Formula: see text] norms, respectively. For the purpose of validation, we estimate the neural reconstruction performance in time and space separately. Experimental testing is carried out on artificial data, simulating stationary and non-stationary EEG signals. Also, validation is accomplished on two real-world databases, one holding Evoked Potentials and another with EEG data of focal epilepsy. Moreover, responses of functional magnetic resonance imaging for the former EEG data have been measured in advance, allowing to contrast our findings. Obtained results show that the [Formula: see text]-based IRA produces a spatial resolution that is comparable to the one achieved by some widely used sparse-based estimators of brain activity. At the same time, the [Formula: see text]-based IRA outperforms other similar smooth solutions, providing a spatial resolution that is lower than the sparse [Formula: see text]-based solution. As a result, the proposed IRA is a promising method for improving the accuracy of brain activity reconstruction.

scholarly journals Detection of Burst Suppression Patterns in EEG Using Recurrence Rate

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhenhu Liang ◽  
Yinghua Wang ◽  
Yongshao Ren ◽  
Duan Li ◽  
Logan Voss ◽  
...  
Keyword(s):  
Recurrence Rate ◽  
Brain Activity ◽  
Approximate Entropy ◽  
Energy Operator ◽  
Burst Suppression ◽  
Multiple Comparison ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Nonlinear Energy ◽  
Selection Of

Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia. It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases. This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests. Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO). ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%, P=0.03). Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.

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