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CLINICAL EEG AND NEUROSCIENCE
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Balance recognition on the basis of EEG measurement.
PublicationAlthough electroencephalography (EEG) is not typically used for verifying the sense of balance, it can be used for analysing cortical signals responsible for this phenomenon. Simple balance tasks can be proposed as a good indicator of whether the sense of balance is acting more or less actively. This article presents preliminary results for the potential of using EEG to balance sensing....
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Tensor Decomposition for Imagined Speech Discrimination in EEG
PublicationMost of the researches in Electroencephalogram(EEG)-based Brain-Computer Interfaces (BCI) are focused on the use of motor imagery. As an attempt to improve the control of these interfaces, the use of language instead of movement has been recently explored, in the form of imagined speech. This work aims for the discrimination of imagined words in electroencephalogram signals. For this purpose, the analysis of multiple variables...
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Metody redukcji artefaktów w zapisie EEG.
PublicationPrzegląd i opis metod badania EEG jego uwarunkowań technicznych oraz problemy z tym związane. Dokonano przeglądu metod pozwalających na zredukowanie bądź eliminacje artefaktów w zapisie EEG.
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Labeler-hot Detection of EEG Epileptic Transients
PublicationPreventing early progression of epilepsy and sothe severity of seizures requires effective diagnosis. Epileptictransients indicate the ability to develop seizures but humansoverlook such brief events in an electroencephalogram (EEG)what compromises patient treatment. Traditionally, trainingof the EEG event detection algorithms has relied on groundtruth labels, obtained from the consensus...
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Decoding imagined speech for EEG-based BCI
PublicationBrain–computer interfaces (BCIs) are systems that transform the brain's electrical activity into commands to control a device. To create a BCI, it is necessary to establish the relationship between a certain stimulus, internal or external, and the brain activity it provokes. A common approach in BCIs is motor imagery, which involves imagining limb movement. Unfortunately, this approach allows few commands. As an alternative, this...
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Transfer learning in imagined speech EEG-based BCIs
PublicationThe Brain–Computer Interfaces (BCI) based on electroencephalograms (EEG) are systems which aim is to provide a communication channel to any person with a computer, initially it was proposed to aid people with disabilities, but actually wider applications have been proposed. These devices allow to send messages or to control devices using the brain signals. There are different neuro-paradigms which evoke brain signals of interest...
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MACHINE LEARNING APPLICATIONS IN RECOGNIZING HUMAN EMOTIONS BASED ON THE EEG
PublicationThis study examined the machine learning-based approach allowing the recognition of human emotional states with the use of EEG signals. After a short introduction to the fundamentals of electroencephalography and neural oscillations, the two-dimensional valence-arousal Russell’s model of emotion was described. Next, we present the assumptions of the performed EEG experiment. Detail aspects of the data sanitization including preprocessing,...
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Comparison of the effectiveness of automatic EEG signal class separation algorithms
PublicationIn this paper, an algorithm for automatic brain activity class identification of EEG (electroencephalographic) signals is presented. EEG signals are gathered from seventeen subjects performing one of the three tasks: resting, watching a music video and playing a simple logic game. The methodology applied consists of several steps, namely: signal acquisition, signal processing utilizing z-score normalization, parametrization and...
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Comparison of Classification Methods for EEG Signals of Real and Imaginary Motion
PublicationThe classification of EEG signals provides an important element of brain-computer interface (BCI) applications, underlying an efficient interaction between a human and a computer application. The BCI applications can be especially useful for people with disabilities. Numerous experiments aim at recognition of motion intent of left or right hand being useful for locked-in-state or paralyzed subjects in controlling computer applications....
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Automatic Clustering of EEG-Based Data Associated with Brain Activity
PublicationThe aim of this paper is to present a system for automatic assigning electroencephalographic (EEG) signals to appropriate classes associated with brain activity. The EEG signals are acquired from a headset consisting of 14 electrodes placed on skull. Data gathered are first processed by the Independent Component Analysis algorithm to obtain estimates of signals generated by primary sources reflecting the activity of the brain....
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Systematic Literature Review for Emotion Recognition from EEG Signals
PublicationResearchers have recently become increasingly interested in recognizing emotions from electroencephalogram (EEG) signals and many studies utilizing different approaches have been conducted in this field. For the purposes of this work, we performed a systematic literature review including over 40 articles in order to identify the best set of methods for the emotion recognition problem. Our work collects information about the most...
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Systematic Literature Review for Emotion Recognition from EEG Signals
PublicationResearchers have recently become increasingly interested in recognizing emotions from electroencephalogram (EEG) signals and many studies utilizing different approaches have been conducted in this field. For the purposes of this work, we performed a systematic literature review including over 40 articles in order to identify the best set of methods for the emotion recognition problem. Our work collects information about the most...
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Method for Clustering of Brain Activity Data Derived from EEG Signals
PublicationA method for assessing separability of EEG signals associated with three classes of brain activity is proposed. The EEG signals are acquired from 23 subjects, gathered from a headset consisting of 14 electrodes. Data are processed by applying Discrete Wavelet Transform (DWT) for the signal analysis and an autoencoder neural network for the brain activity separation. Processing involves 74 wavelets from 3 DWT families: Coiflets,...
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Deep learning approach on surface EEG based Brain Computer Interface
PublicationIn this work we analysed the application of con-volutional neural networks in motor imagery classification for the Brain Computer Interface (BCI) purposes. To increase the accuracy of classification we proposed the solution that combines the Common Spatial Pattern (CSP) with convolutional network (ConvNet). The electroencephalography (EEG) is one of the modalities we try to use for controlling the prosthetic arm. Therefor in this...
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Comparison of Methods for Real and Imaginary Motion Classification from EEG Signals
PublicationA method for feature extraction and results of classification of EEG signals obtained from performed and imagined motion are presented. A set of 615 features was obtained to serve for the recognition of type and laterality of motion using 8 different classifications approaches. A comparison of achieved classifiers accuracy is presented in the paper, and then conclusions and discussion are provided. Among applied algorithms the...
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Pursuing the Deep-Learning-Based Classification of Exposed and Imagined Colors from EEG
PublicationEEG-based brain-computer interfaces are systems aiming to integrate disabled people into their environments. Nevertheless, their control could not be intuitive or depend on an active external stimulator to generate the responses for interacting with it. Targeting the second issue, a novel paradigm is explored in this paper, which depends on a passive stimulus by measuring the EEG responses of a subject to the primary colors (red,...
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Rough Set-Based Classification of EEG Signals Related to Real and Imagery Motion
PublicationA rough set-based approach to classification of EEG signals registered while subjects were performing real and imagery motions is presented in the paper. The appropriate subset of EEG channels is selected, the recordings are segmented, and features are extracted, based on time-frequency decomposition of the signal. Rough set classifier is trained in several scenarios, comparing accuracy of classification for real and imagery motion....
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Brain-computer interaction based on EEG signal and gaze-tracking information = Analiza interackji mózg-komputer wykorzystująca sygnał EEg i informacje z systemu śledzenia punktu fiksacji wzroku
PublicationThe article presents an attempt to integrate EEG signal analysis with information about human visual activities, i.e. gaze fixation point. The results from gaze-tracking-based measurement were combined with the standard EEG analysis. A search for correlation between the brain activity and the region of the screen observed by the user was performed. The preliminary stage of the study consists in electrooculography (EOG) signal processing....
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Analysis of the Capability of Deep Learning Algorithms for EEG-based Brain-Computer Interface Implementation
PublicationMachine learning models have received significant attention for their exceptional performance in classifying electroencephalography (EEG) data. They have proven to be highly effective in extracting intricate patterns and features from the raw signal data, thereby contributing to their success in EEG classification tasks. In this study, we explore the possibilities of utilizing contemporary machine learning algorithms in decoding...
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Rating by detection: an artifact detection protocol for rating EEG quality with average event duration
PublicationQuantitative evaluation protocols are critical for the development of algorithms that remove artifacts from real EEG optimally. However, visually inspecting the real EEG to select the top-performing artifact removal pipeline is infeasible while hand-crafted EEG data allow assessing artifact removal configurations only in a simulated environment. This study proposes a novel, principled approach for quantitatively evaluating algorithmically...
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Real and imaginary motion classification based on rough set analysis of EEG signals for multimedia applications
PublicationRough set-based approach to the classification of EEG signals of real and imaginary motion is presented. The pre-processing and signal parametrization procedures are described, the rough set theory is briefly introduced, and several classification scenarios and parameters selection methods are proposed. Classification results are provided and discussed with their potential utilization for multimedia applications controlled by the...
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Intra-subject class-incremental deep learning approach for EEG-based imagined speech recognition
PublicationBrain–computer interfaces (BCIs) aim to decode brain signals and transform them into commands for device operation. The present study aimed to decode the brain activity during imagined speech. The BCI must identify imagined words within a given vocabulary and thus perform the requested action. A possible scenario when using this approach is the gradual addition of new words to the vocabulary using incremental learning methods....
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Wspomaganie komunikacji w procesie neurorehabilitacji z wykorzystaniem śledzenia wzroku i analizy sygnałów EEG
PublicationW pracy przedstawiono charakterystykę systemu do wspomagania komunikacji w procesie neurorehabilitacji osób w stanie ograniczonej świadomości. Przygotowana aplikacja komputerowa wykorzystuje metodę śledzenia wzroku wspomaganą analizą sygnału EEG. W pracy podano genezę powstania systemu, scharakteryzowano zaimplementowane ćwiczenia oraz pozostałe funkcjonalności, a także zamieszczono wyniki wstępnych badań dokonanych w kilku polskich...
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Independent dynamics of slow, intermediate, and fast intracranial EEG spectral activities during human memory formation
PublicationA wide spectrum of brain rhythms are engaged throughout the human cortex in cognitive functions. How the rhythms of various low and high frequencies are spatiotemporally coordinated across the human brain during memory processing is inconclusive. They can either be coordinated together across a wide range of the frequency spectrum or induced in specific bands. We used a large dataset of human intracranial electroencephalography...
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Independent dynamics of slow, intermediate, and fast intracranial EEG spectral activities during human memory formation
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EEG data recorded in three mental states
Open Research DataElectroencephalographic (EEG) signals were acquired from 17 (14 males, 3 females) participants aged between 20 and 30 years.
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Wykorzystanie rzeczywistego zapisu EEG w modelu potencjałów wywołanych do testowania algorytmów detekcji potencjałów wzrokowych.
PublicationPrzedstawiona praca dotyczy kontynuacji badań modelowych, gdzie syntetyczna aktywność spontaniczna została zastąpiona sygnałem rzeczywistym. Ocenie podlega uprzednio stworzony model aktywności spontanicznej. Weryfikacji ulega przydatność algorytmu detekcji.
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A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection
PublicationComputational cost is an important consideration for memory encoding prediction models that use data from dozens of implanted electrodes. We propose a method to reduce computational expense by selecting a subset of all the electrodes to build the prediction model. The electrodes were selected based on their likelihood of measuring brain activity useful for predicting memory encoding better than chance (in terms of AUC). A logistic...
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Independent dynamics of low, intermediate, and high frequency spectral intracranial EEG activities during human memory formation
PublicationA wide spectrum of brain rhythms are engaged throughout the human cortex in cognitive functions. How the rhythms of various frequency ranges are coordinated across the space of the human cortex and time of memory processing is inconclusive. They can either be coordinated together across the frequency spectrum at the same cortical site and time or induced independently in particular bands. We used a large dataset of human intracranial...
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Fast and Efficient Four-Class Motor Imagery EEG Signals Analysis Using CSP-Ridge Regression Algorithm for the Purpose of Brain-Computer Interface."
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The set of 22 sessions of 14-channel eeg signals recorded during watching pictures
Open Research DataThe data were collected in order to perform research on the possibility of controlling the content displayed on the monitor screen using human emotional states extracted from EEG signals. The dataset contains recordings of 14-channel EEG signals collected from 10 persons within 22 sessions, during which 45 different random photos taken from the ImageNet...
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An approach to adaptive beamforming in measurement of EEG.Proceedings 2nd European Medical and Biological Engineering Conference.EMBEC´02.Pt.2
PublicationPotencjały wywołane są wykorzystywane w diagnostyce narządów zmysłu. Ze względu na niski SNR wymagana jest wielokrotne powtarzanie stymulacji i uśrednianie wyników. Prowadzi to do zwielokrotnienia czasu badania. SNR może zostać poprawiony przy użyciu matrycy sensorów. Są one wykorzystane do śledzenia źródeł sygnału. W publikacji przedstawiono dwie metody adaptacyjnego poprawania współczynnika SNR.
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NeuroWeave Advanced algorithms for identification of electrophysiological features underlying encoding and recall of human memory in intracranial EEG
ProjectsProject realized in Department of Multimedia Systems according to UMO-2021/03/Y/NZ4/00082 agreement from 2022-08-29
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Exploring the technological dimension of Autonomous sensory meridian response-induced physiological responses
PublicationBackground In recent years, the scientific community has been captivated by the intriguing Autonomous sensory meridian response (ASMR), a unique phenomenon characterized by tingling sensations originating from the scalp and propagating down the spine. While anecdotal evidence suggests the therapeutic potential of ASMR, the field has witnessed a surge of scientific interest, particularly through the use of neuroimaging techniques...
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STEADY STATE VISUALLY EVOKED POTENTIALS FOR BRAIN COMPUTER INTERFACE
PublicationAn experiment conducted to validate a possibility of use a single active electrode EEG device for detecting Steady State Visually Evoked Potentials (SSVEP) is shown. A LED stimulator was applied to stimulate patients with two different frequencies - 13 Hz and 17 Hz. First, EEG signals were recorded and pre-processed using MATLAB software. In the next step recordings were analysed and classified employing the WEKA software. As indicated...
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Comparison of selected electroencephalographic signal classification methods
PublicationA variety of methods exists for electroencephalographic (EEG) signals classification. In this paper, we briefly review selected methods developed for such a purpose. First, a short description of the EEG signal characteristics is shown. Then, a comparison between the selected EEG signal classification methods, based on the overview of research studies on this topic, is presented. Examples of methods included in the study are: Artificial...
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Consciousness Study of Subjects with Unresponsive Wakefulness Syndrome Employing Multimodal Interfaces
PublicationThe paper presents a novel multimodal-based methodology for consciousness study of individuals with unresponsive wakefulness syndrome. Two interfaces were employed in the experiments: eye gaze tracking system – CyberEye developed at the Multimedia Systems Department, and EEG device with electrode placement in the international 10-20 standard. It was a pilot study for checking if it is possible to determine objective methods based...
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Neural oscillations induced by IAPS pictures (session 24)
Open Research DataThe data were collected to perform research on the possibility of controlling the content displayed on the monitor screen using human emotional states extracted from EEG signals. The dataset contains recordings of 14-channel EEG signals gathered from seven persons aged 23 - 35 within 26 sessions, during which 45 different random photos, taken from the...
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Neural oscillations induced by IAPS pictures (session 23)
Open Research DataThe data were collected to perform research on the possibility of controlling the content displayed on the monitor screen using human emotional states extracted from EEG signals. The dataset contains recordings of 14-channel EEG signals gathered from seven persons aged 23 - 35 within 26 sessions, during which 45 different random photos, taken from the...
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Neural oscillations induced by IAPS pictures (session 25)
Open Research DataThe data were collected to perform research on the possibility of controlling the content displayed on the monitor screen using human emotional states extracted from EEG signals. The dataset contains recordings of 14-channel EEG signals gathered from seven persons aged 23 - 35 within 26 sessions, during which 45 different random photos, taken from the...
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Neural oscillations induced by IAPS pictures (session 26)
Open Research DataThe data were collected to perform research on the possibility of controlling the content displayed on the monitor screen using human emotional states extracted from EEG signals. The dataset contains recordings of 14-channel EEG signals gathered from seven persons aged 23 - 35 within 26 sessions, during which 45 different random photos, taken from the...
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Zastosowanie danych syntetycznych do testowania algorytmów detekcji potencjałów wzrokowych.
PublicationDetekcja wywołanych potencjałów wzrokowych w perymetrii wymaga opracowania i zaaplikowania efektywnych algorytmów analizy EEG. Krokiem pośrednim, umożliwiającym ocenę przydatności nowego algorytmu może być modelowa symulacja sygnałów. Przedstawiona praca dotyczy badań modelowych będących krokiem pośrednim we wdrażaniu nowego algorytmu detekcji potencjałów wzrokowych w celu doskonalenia techniki badania perymetrycznego.
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Assessment of Therapeutic Progress After Acquired Brain Injury Employing Electroencephalography and Autoencoder Neural Networks
PublicationA method developed for parametrization of EEG signals gathered from participants with acquired brain injuries is shown. Signals were recorded during therapeutic session consisting of a series of computer assisted exercises. Data acquisition was performed in a neurorehabilitation center located in Poland. The presented method may be used for comparing the performance of subjects with acquired brain injuries (ABI) who are involved...
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How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Image
PublicationThis study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation...
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Influence of the Spectral Quality of Light on Daytime Alertness Levels in Humans
PublicationExposure to light is very important for human health. However, the characteristics of the light stimulus and the appropriate timing of such exposure are essential. Studies that have used monochromatic light exposure have shown no systematic patterns for the effects of blue light compared to longer wavelengths. Previous studies have shown that red light exposure increases objective and subjective measures of alertness at night without...
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Development of visual evoked potentials detection algorithm for objective perimetry
PublicationOpisano nową propozycję algorytmu detekcji potencjałów wzrokowych w zapisie EEG. Nowy algorytm bazuje na dekompozycji statystycznej ICA. Algorytm wstępnie przetestowano na danych eksperymentalnych.
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Power of the low alpha brainwaves in the mental imagery experiment in sport: the "Your Home Venue" scenario.
Open Research DataThe data were collected to perform research on the neural oscillation during mental imagery in sport. The study's main aim was to examine the cortical correlations of imagery depending on instructional modality (guided vs self-produced) using various sport-related scripts. The research was based on the EEG signals recorded during the session with the...
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Power of the low alpha brainwaves in the mental imagery experiment in sport: the "Slow Start" scenario.
Open Research DataThe data were collected to perform research on the neural oscillation during mental imagery in sport. The main aim of the study was to examine the cortical correlates of imagery depending on instructional modality (guided vs self-produced) using various sport-related scripts. The research was based on the EEG signals recorded during the session with...
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Power of the SMR brainwaves in the mental imagery experiment in sport: the "Start in High Level Championship" scenario.
Open Research DataThe data were collected to perform research on the neural oscillation during mental imagery in sport. The main aim of the study was to examine the cortical correlates of imagery depending on instructional modality (guided vs self-produced) using various sport-related scripts. The research was based on the EEG signals recorded during the session with...