Search
Search results for: eeg signal
-
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...
-
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....
-
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....
-
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...
-
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...
-
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,...
-
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...
-
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....
-
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...
-
Fast and Efficient Four-Class Motor Imagery EEG Signals Analysis Using CSP-Ridge Regression Algorithm for the Purpose of Brain-Computer Interface."
Publication -
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...
-
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...
-
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...
-
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...
-
Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning
PublicationThe aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was...
-
Employing a biofeedback method based on hemispheric synchronization in effective learning
PublicationIn this paper an approach to build a brain computer-based hemispheric synchronization system is presented. The concept utilizes the wireless EEG signal registration and acquisition as well as advanced pre-processing methods. The influence of various filtration techniques of EOG artifacts on brain state recognition is examined. The emphasis is put on brain state recognition using band pass filtration for separation of individual...
-
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.
-
Daytime Acute Non-Visual Alerting Response in Brain Activity Occurs as a Result of Short- and Long-Wavelengths of Light
PublicationVery recent preliminary findings concerning the alerting capacities of light stimulus with long-wavelengths suggest the existence of neural pathways other than melatonin suppression that trigger the nonvisual response. Though the nonvisual effects of light during the daytime have not been investigated thoroughly, they are definitely worth investigating. The purpose of the present study is to enrich existing evidence by describing...
-
Problems in estimation of hand grip force based on EMG signal
PublicationThere has recently been a significant increase in the number of publications on and applications of bioelectric signals for diagnostic purposes. While the use of ECG (electrocardiography) is not surprising, the use of signals from registration of brain activity (EEG) and muscles activity (EMG) still finds new applications in various fields. The authors focus on the use of EMG signals for estimating hand grip force. Currently,...
-
Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance
PublicationIdentification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_004)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_005)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_0010)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_002)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_0012)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_008)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_003)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_007)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_006)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_0011)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_009)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_0017)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_0021)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_0020)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_0019)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_0024)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
Invasive electrophysiological patient recordings from the human brain during memory tasks with pupilometry (MC_0023)
Open Research DataData comprise intracranial EEG (iEEG) brain activity, including electrocorticography (ECoG) signals, recorded from over 100 electrodes implanted in one patient throughout various brain regions. These iEEG signals were recorded in epilepsy patients undergoing invasive monitoring and localization of seizures when they were performing a battery of four...
-
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....
-
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.
-
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....
-
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....
-
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...
-
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.
-
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...
-
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,...
-
Synchronizacja półkul mózgowych z wykorzystaniem mechanizmu biofeedback
PublicationW niniejszej pracy zaproponowane zostały dwa indywidualne podejścia do budowy systemu wspomagającego synchronizację półkul mózgowych przy pomocy mechanizmu biofeedback. Dla obu rozwiązań wykorzystane zostało urządzenie wykorzystujące bezprzewodowy system rejestracji sygnałów EEG. W pierwszym podejściu sprawdzono wpływ dudnień różnicowych na stan synchronizacji z zastosowaniem statystycznych metod analizy. W drugiej metodzie zbadane...
-
Multimodal Approach For Polysensory Stimulation And Diagnosis Of Subjects With Severe Communication Disorders
Publicationis evaluated on 9 patients, data analysis methods are described, and experiments of correlating Glasgow Coma Scale with extracted features describing subjects performance in therapeutic exercises exploiting EEG and eyetracker are presented. Performance metrics are proposed, and k-means clusters used to define concepts for mental states related to EEG and eyetracking activity. Finally, it is shown that the strongest correlations...
-
Intracranial electrophysiological recordings from the human brain during memory tasks with pupillometry
PublicationData comprise intracranial EEG (iEEG) brain activity represented by stereo EEG (sEEG) signals, recorded from over 100 electrode channels implanted in any one patient across various brain regions. The iEEG signals were recorded in epilepsy patients (N=10) undergoing invasive monitoring and localization of seizures when they were performing a battery of four memory tasks lasting approx. 1 hour in total. Gaze tracking on the task...
-
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...
-
Multifactor consciousness level assessment of participants with acquired brain injuries employing human–computer interfaces
PublicationBackground A lack of communication with people suffering from acquired brain injuries may lead to drawing erroneous conclusions regarding the diagnosis or therapy of patients. Information technology and neuroscience make it possible to enhance the diagnostic and rehabilitation process of patients with traumatic brain injury or post-hypoxia. In this paper, we present a new method for evaluation possibility of communication and the...