Real Time EEG

Recent years have seen huge advancements in the methods available and used in neuroscience employing EEG or MEG. However, the standard approach is to average a large number of trials for experimentally defined conditions in order to reduce intertrial-variability, i.e. treating it as a source of "noise". Yet it is now more and more accepted that trial-to-trial fluctuations bear functional significance, reflecting fluctuations of "brain states" that predispose perception and action. Such effects are often revealed in a pre-stimulus period, when comparing response variability to an invariant stimulus. However such offline analyses are disadvantageous as they are correlational by drawing conclusions in a posthoc-manner and stimulus presentation is random with respect to the feature of interest. A more direct test is to trigger stimulus presentation when the relevant feature is present. The current paper introduces ConSole (CONstance System for OnLine Eeg), a software...

The basis of the work of electroencephalography (EEG) is the registration of electrical impulses from the brain using a special sensor or electrode. This method is used to treat and diagnose various diseases. In the past few years, due to the development of neural network technologies, the interest of researchers in EEG has noticeably increased. Neural networks for training the model require obtaining data with minimal noise distortion. In the processing of EEG signals to eliminate noise (artifacts), signal filtering and various methods for extracting signs are used. The presented manuscript provides a detailed analysis of modern methods for extracting the signs of an EEG signal used in studies of the last decade. The information presented in this paper will allow researchers to understand how to more carefully process the data of EEG signals before using neural networks to classify the signal. Due to the absence of any standards in the method of extracting EEG signs, the most important moment of this manuscript is a detailed description of the necessary steps for recognizing artifacts, which will allow researchers to maximize the potential of neural networks in the tasks of classifying EEG signal. IJSB

Architectural design is a complex process. In order to shed light on this process, the idea of investigating human responses to design has been contemplated before. This approach has recently been consolidated into the widely recognized paradigm of evidence-based design (EBD). Despite the evident benefits of the EBD approach, its adoption specially in architectural research remains limited. The resulting gap in knowledge of the relationship between the human mind and the built environment was the main source of motivation for this thesis.
Recent academic and technological advancements provided great opportunities to explore new methods for expanding our knowledge of the mind in space. On one hand, the theory of embodied cognition rejects a strict separation between mind, body, and its environment and recently has become the cornerstone of many architectural studies. It has been proven that architectural affordances directly influence behavior and brain activity, and this activity can be recorded through low-cost commercial EEG devices. On the other hand, to achieve more accurate and reliable results, a controlled environment is needed that enables easy, isolated, and controlled changes. Besides, it should replicate the users’ responses as close as possible to a real situation. It appears that virtual reality (VR) has the potential to capture all the criteria for the aforementioned environment. Consequently, the idea of a proof of concept was formed to provide further evidence for use of neurofeedback in architectural design as a novel method to achieve a deeper understanding of architectural EBD. Therefore, a prototype was developed to assess whether capabilities of the low-cost commercial EEG equipment and an affordable VR headset are sufficient to create an evaluative toolbox for architects to run an experiment on the experience of spaces.
The objective of this study was to gain a deeper understanding of how design concepts in a built environment influence human perception through reimagining the design process. Therefore, it can provide architects with a deeper insight, in a subjective manner, of the stakeholders’ preferences before diving into the design process. We should consider that, with a higher number of experiments, a statistical analysis of the findings will be possible, which can lead to a more objectified point of view and subsequently a set of design recommendations.

In recent years, neural networks showed unprecedented growth that ultimately influenced dozens of different industries, including signal processing for the electroencephalography (EEG) process. Electroencephalography, although it appeared in the first half of the 20th century, was not changed the physical principles of work to this day. But signal processing technology made significant progress in this area through the use of neural networks. But many different models of neural networks complicate the process of understanding the real situation in this area. This manuscript summarizes the current state of knowledge on this topic, summarizes and describes the most significant achievements in various fields of application of neural networks for processing EEG signals. We discussed in detail the results presented in recent research papers for various fields in which EEG signals have been involved. We also examined in detail the process of extracting features from EEG signals using neural networks. In conclusion, we have provided recommendations for the correct demonstration of research results in manuscripts on the subject of neural networks and EEG.

The primary goal in cognitive neuroscientific research is to investigate the underlying neurobiological mechanisms that inaugurate various behaviors and executive functions. A multitude of methodologies exists today that can be used to measure cortical electrical activities. Electroencephalography (EEG) has now been established as an essential noninvasive measure for investigating cortical electric fields. The use of Cognionics dry-mobile wireless EEG system has become widely popular in cognitive neuroscientific research due to its portability, ease of setting-up, and easy access to the raw data for further processing. This paper presents a brief guideline for exploiting Cognionics HD-72 wireless EEG systems in psychological research without incurring a performance penalty during data collection. A careful investigation of the components in the EEG system will allow researchers to utilize the system effectively when conducting EEG research.

There are so far two main methodological approaches for rejecting ocular artifacts from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals: regression- and Blind Source Separation (BSS)-based techniques, both having merits, as well as, some serious limitations. In this piece of work, a hybrid methodology that combines the main advantages of these two methods is proposed. We hypothesize that the artifactual independent components (ICs) extracted by a BSS method include more ocular and less cerebral activity than the contaminated EEG signals. We thus propose to apply a regression algorithm to the ICs rather than directly to the recorded signals. The analysis was carried out with synthetic mixtures of real EEG and electroocculographic (EOG) recordings. A BSS method, the extended INFOMAX version of Independent Component Analysis (ICA), was initially used to decompose the artificially contaminated EEG signals into spatiotemporal ICs. Then, a regression scheme, based on a stable version of the Recursive Least Squares algorithm, sRLS, was applied to the artifactual components in order to remove only the ocular artifacts, maintaining the underlying neural signals intact. The processed ICs were then projected back, reconstructing the artifact-free EEG signals. The performance of the proposed technique was compared with two automatic techniques; a regression technique based on Least Mean Square (LMS) algorithm and a BSS-based artifact rejection technique called wavelet-ICA (W-ICA) on the artificially contaminated data. For comparison, two metrics were used to assess the different methods’ performance: the first quantified how successful each technique was in removing the ocular artifacts from the EEG recordings, and the second one quantified how much each technique distorted the ongoing brain activity in both time and frequency domains. Confirming our main hypothesis, results have shown that the artifactual ICs contained more ocular and less cerebral activity (p < 0.04) (artifact to signal ratio (ASR) = 1.83 ± 3.65) in contrast to the contaminated electrode signals (ASR = 0.69 ± 3.40). Our results reveal that the proposed methodology, namely REG-ICA, removes the ocular artifacts more successfully than W-ICA (p < 0.01) or LMS (p < 0.01). It also distorts less the brain activity in the time domain when compared to W-ICA and LMS. In the frequency domain, it distorts the brain activity less than the W-ICA in all frequency bands, and less than the LMS for the delta, beta, and gamma bands. Our results suggest that the proposed methodology is evidently an attractive alternative to other already proposed artifact rejection methodologies.

Recent years have seen huge advancements in the methods available and used in neuroscience employing EEG or MEG. However, the standard approach is to average a large number of trials for experimentally defined conditions in order to reduce intertrial-variability, i.e. treating it as a source of "noise". Yet it is now more and more accepted that trial-to-trial fluctuations bear functional significance, reflecting fluctuations of "brain states" that predispose perception and action. Such effects are often revealed in a pre-stimulus period, when comparing response variability to an invariant stimulus. However such offline analyses are disadvantageous as they are correlational by drawing conclusions in a posthoc-manner and stimulus presentation is random with respect to the feature of interest. A more direct test is to trigger stimulus presentation when the relevant feature is present. The current paper introduces ConSole (CONstance System for OnLine Eeg), a software package capable of analyzing ongoing EEG / MEG in real-time and presenting auditory and visual stimuli via internal routines. Stimulation via external devices (e.g. TMS) or third-party software (e.g. Psyscope X) is possible by sending TTL-triggers. With ConSole it is thus possible to target the stimulation at specific brain-states. In contrast to many available applications, ConSole is open-source. Its modular design enhances the power of the software as it can be easily adapted to new challenges and writing new experiments is an easy task. ConSole is already pre-equipped with modules performing standard signal processing steps. The software is also independent from the EEG / MEG system, as long as a driver can be written (currently 2 EEG systems are supported). Besides a general introduction, we present benchmark data regarding performance and validity of the calculations used, as well as three example applications of ConSole in different settings. ConSole can be downloaded at: http://console-kn.sf.net.