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.

Effects of map design characteristics on users’ search performance and cognitive load

2019 ◽  
Vol 37 (4) ◽  
pp. 667-679 ◽  
Author(s):  
Hosam Al-Samarraie ◽  
Atef Eldenfria ◽  
Melissa Lee Price ◽  
Fahed Zaqout ◽  
Wan Mohamad Fauzy
Keyword(s):  
Cognitive Load ◽  
Brain Activity ◽  
Cognitive Activity ◽  
Search Performance ◽  
Brain Wave ◽  
Content Type ◽  
Eeg Data ◽  
Map Design ◽  
Design Characteristics ◽  
Time Required

Purpose This paper aims to investigate the influence of map design characteristics on users’ cognitive load and search performance. Two design conditions (symbolic vs non-symbolic) were used to evaluate users’ ability to locate a place of interest. Design/methodology/approach A total of 19 students (10 male and 9 female, 20-23 years old) participated in this study. The time required for subjects to find a place in the two conditions was used to estimate their searching performance. An electroencephalogram (EEG) device was used to examine students’ cognitive load using event-related desynchronization percentages of alpha, beta and theta brain wave rhythms. Findings The results showed that subjects needed more time to find a place in the non-symbolic condition than the symbolic condition. The EEG data, however, revealed that users experienced higher cognitive load when searching for a place in the symbolic condition. The authors found that the design characteristics of the map significantly influenced users’ brain activity, thus impacting their search performance. Originality/value Outcomes from this study can be used by cartographic designers and scholars to understand how certain design characteristics can trigger cognitive activity to improve users' searching experience and efficiency.

scholarly journals Artifacts in EEG-based BCI therapies: friend or foe?

2021 ◽  
Author(s):  
Eric James McDermott ◽  
Philipp Raggam ◽  
Sven Kirsch ◽  
Paolo Belardinelli ◽  
Ulf Ziemann ◽  
...  
Keyword(s):  
Therapeutic Potential ◽  
Brain Activity ◽  
Mental States ◽  
Extraction Methods ◽  
Brain State ◽  
Eeg Signal ◽  
Motor Tasks ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Brain States

EEG-based brain-computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain-states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data was collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifact component of the EEG signal is significantly more informative than brain activity to the classifier. This finding is consistent across different feature extraction methods and classification pipelines. Whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

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 Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

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 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.

Recognition of true and false states of the brain by means of the electroencephalogram wavelet analysis

2021 ◽  
Author(s):  
S.S. Pertsov ◽  
E.A. Yumatov ◽  
N.A. Karatygin ◽  
E.N. Dudnik ◽  
A.E. Khramov ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Experimental Model ◽  
Continuous Wavelet Transform ◽  
Brain Activity ◽  
Mental States ◽  
Mental Activity ◽  
Continuous Wavelet ◽  
Wavelet Energy ◽  
True State ◽  
The Brain

It is a well-known fact that mental activity of the brain can be presented by two different states, i.e., the true state and the false state. A promising method of the electroencephalogram (EEG) wavelet transform has been developed over recent years. Using this method, we evaluated the principle possibility for direct objective registration of mental activity in the human brain. Previously we developed and described (published) a new experimental model and software for recognizing the true and false mental responses of a person with the EEG wavelet transform. The developed experimental model and software-and-data support allowed us to compare (by EEG parameters) two mental states of brain activity, one of which is the false state, while another is the true state. The goal of this study is to develop an absolutely new information technology for recognizing the true and false states in mental activity of the brain by means of the EEG wavelet transform. Our study showed that the true and false states of the brain can be distinguished using the method of continuous wavelet transform and calculation of the EEG wavelet energy. It was revealed that the main differences between truthful and false mental responses are observed in the delta and alpha ranges of the EEG. In the EEG delta rhythm, the wavelet energy is much higher under conditions of the false response as compared to that in the true response. In the EEG alpha rhythm, the wavelet energy is significantly higher with the true answer than in the false one. These data open a new principal possibility of revealing the true and false mental state of the brain by means of continuous wavelet transform and calculation of the EEG wavelet energy.

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.

Streamlining Service Platform for Integrating IoT Services

2018 ◽  
pp. 106-138
Keyword(s):  
Web Services ◽  
Impedance Matching ◽  
Data Access ◽  
Service Systems ◽  
Smart Devices ◽  
Service Access ◽  
Service Platform ◽  
Iot Applications ◽  
Distributed Execution ◽  
Event Based

IoT scenarios involve both smart devices hosting web services and very simple devices with external web services. Without unified access to these types of devices, the construction of IoT service systems would be cumbersome. The basic principle of this chapter is the integration of distributed events into SOA. The data access capability of physical entities is first separated from their actuation capability, which acts as a foundation for ultra-scale and elastic IoT applications. Then, a distributed event-based IoT service platform is established to support the creation of IoT services and allow the hiding of service access complexity, where the IoT services are event-driven; the design goals are impedance matching between service computation and event communication. The coordination logic of an IoT service system is extracted as an event composition that supports the distributed execution of the system and offers scalability. Finally, an application is implemented on the platform to demonstrate its effectiveness and applicability.

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