Wyniki wyszukiwania - BazTech

1

Application of bioelectrical brain activity research to assess the level of attention of a farm tractor operator

Trzyniec Karolina, Kądzioła Dawid, Stachurska Maria

Przegląd Elektrotechniczny

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2023

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R. 99, nr 1

319--322

EN

The aim of the research was to analyze the attention of a farm tractor operators during work performed in the field. The scope of work included the testing of the level of EEG signals in 4 drivers while driving a tractor with simultaneous operation of the navigation system and the analysis of the obtained results. Drivers' EEG signals were tested with the Emotiv Epoc Flex neuro-helmet. The level of beta waves (Low Beta and High Beta) responsible for attention was determined and the variability of the level was analyzed.

PL

Celem badań była analiza uwagi operatorów ciągnika rolniczego podczas prac polowych. Zakres prac obejmował badanie poziomu sygnałów EEG u 4 kierowców podczas jazdy ciągnikiem przy jednoczesnej pracy systemu nawigacyjnego oraz analizę uzyskanych wyników. Sygnały EEG kierowców zostały przetestowane za pomocą neurohełmu Emotiv Epoc Flex. Określono poziom fal beta (Low Beta i High Beta) odpowiedzialnych za uwagę i przeanalizowano zmienność poziomu.

2

Technologia CyberOko do diagnozy, rehabilitacji i komunikowania się z pacjentami niewykazującymi oznak przytomności

Czyżewski Andrzej, Odya Piotr, Szczuko Piotr, Kostek Bożena, Kwiatkowska Agnieszka, Lech Michał, Kucewicz Michał

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

|

2022

|

nr 3

60--69

PL

CyberOko jest rozwiązaniem opracowanym w Politechnice Gdańskiej, które umożliwia nawiązanie kontaktu i pracę z osobami głęboko upośledzonymi komunikacyjnie. W sposób inteligentny śledzi ruch gałek ocznych, dzięki czemu umożliwia rehabilitację i ocenę stanu świadomości pacjenta nawet w stanie całkowitego porażenia. Rozwiązanie obejmuje także analizę fal EEG, obiektywne badanie słuchu i badanie sygnałów z macierzy elektrod wszczepianych w głąb ludzkiego mózgu. Wspomaga komunikację z pacjentami niewykazującymi oznak przytomności i ich dalszą rehabilitację sposobami umożliwiającymi pokonanie istotnych ograniczeń, jakie mają metody i technologie będące w powszechnym użyciu, tzn. subiektywne skale ocen pacjentów (np. ocena w skali GCS – Glasgow Conciousness Scale), badanie procesów pamięciowych wewnątrz mózgu ludzkiego. Wdrożone urządzenie jest często jedyną szansą dla osoby chorej (np. w stanach podobnych do śpiączki, w przetrwałym stanie wegetatywnym, osoby sparaliżowanej, bez możliwości mówienia), aby mogła ona wyrazić swoje potrzeby.

EN

CyberOko (CyberEye) is a pioneering solution developed at the Gdansk University of Technology enabling contact and work with people with profound communication disabilities. It intelligently tracks eye movements, allowing for rehabilitation and assessment of the patient's state of consciousness even in a state of profound paralysis or locked-in syndrome. The technology engineered also includes the analysis of EEG waves, objective hearing testing, and examination of signals from an array of electrodes implanted deep into the human brain. It supports communication with unconscious patients and their further rehabilitation by means that overcome significant limitations of the methods and technologies in common use, i.e. subjective patient rating scales such as, e.g., Glasgow Conciousness Scale (GCS), study of memory processes inside the human brain. The device implemented is often the only chance for the sick person (e.g., in coma-like states, in a persistent vegetative state, paralyzed, unable to speak) to express their needs.

3

Analysis of the application of brain-computer interfaces of a selected paradigm in everyday life

Mróz Katarzyna, Plechawska-Wójcik Małgorzata

Journal of Computer Sciences Institute

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2022

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Vol. 23

118--122

EN

The main objective of this paper is to carry out a research on the analysis of the use of brain-computer interface in everyday life. The article presents the method of recording brain activity, electroencephalography, which was used in the study. The brain activity used in the brain-computer interface and the general principle of brain-computer interface design are also described. The performed study allowed to develop an analysis of the obtained results in the matter of evaluating the usability of brain-computer interfaces using motor imagery. As a result of the process of analyzing the results obtained during the research, it was found that each subsequent experiment allowed for obtaining more favourable results than the previous one. The reason for this was the use of an additional training session for the next test person. In the final stage, it was possible to evaluate the usability of the brain-computer interface in everyday life

PL

Głównym celem artykułu jest przeprowadzenie badania nad analizą wykorzystania interfejsu mózg-komputer w życiu codziennym. W artykule przedstawiono metodę rejestrowania aktywności mózgu, elektroencefalografię, która została wykorzystana w badaniu. Opisano również aktywność mózgu wykorzystywaną w interfejsie mózg-komputer oraz ogólną zasadę projektowania interfejsu mózg-komputer. Przeprowadzone badanie pozwoliło na opracowanie analizy uzyskanych wyników w zakresie oceny użyteczności interfejsów mózg-komputer z wykorzystaniem obrazowania motorycznego. W wyniku procesu analizy wyników uzyskanych podczas przeprowadzania badań ustalono, iż każdy następnie zrealizowany eksperyment pozwalał na uzyskanie korzystniejszych wyników od poprzedniego. Powodem tego było zastosowanie dodatkowej sesji treningowej dla kolejnych badanych osób. W końcowym etapie można było ocenić przydatność interfejsu mózg-komputer w życiu codziennym

4

EEG_GENet: A feature-level graph embedding method for motor imagery classification based on EEG signals

Wang Huiyang, Yu Hua, Wang Haixian

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 3

1023--1040

EN

In recent years, the success of deep learning has driven the development of motor imagery brain-computer interfaces (MI-BCIs) based on electroencephalography (EEG). However, unlike image or language data, motor imagery EEG signals are of multielectrodes with topology information. As a means of integrating graph topology information into feature maps, few studies studied motor imagery classification involving graph embeddings. To decode EEG signals more accurately, this paper proposes a feature-level graph embedding method and combines the method with EEGNet; this new network is called EEG_GENet. Specifically, time-domain features are obtained by convoluting raw EEG signals for each electrode. Then, the adjacent matrix, conceptualized as a graph filter, performs graph convolution and uses the time-domain features to embed the topology information. This process can also perform multi-order graph embeddings. In addition, the adjacency matrix in this paper can adapt to different brain network connectivities for different subjects. We evaluate the proposed method on two benchmark EEG datasets for motor imagery classification. Experimental results on the BCICIV-2a and High_Gamma datasets demonstrate that EEG_GENet achieves 79.57% and 96.02% classification accuracy, respectively. These results indicate that the proposed method is superior to state-of-the-art methods. In addition, various ablation experiments further verify the advantages of the feature-level graph embedding method. To conclude, the feature-level graph embedding method can improves the network’s ability to decode raw motor imagery EEG signals.

5

EEG based cortical investigation for the limit of stability analysis in transfemoral amputees: A comparison with able-bodied individuals

Khajuria Aayushi, Joshi Deepak

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

362--374

EN

Background: The evidences for demonstrating the contributions of the cerebral cortex in human postural control is increasing. However, there remain little insights about the cortical correlates of balance control in lower-limb amputees. The present study aimed to investigate the cortical activity and balance performance of transfemoral amputees in comparison to healthy individuals during a continuous balance task (CBT). Methods: The postural stability of the participants was defined with limit of stability parameter. Electroencephalography (EEG) data were recorded in synchronization with the center of pressure (CoP) data from eighteen individuals (including eight unilateral transfemoral amputees). We anticipated that, due to the limb loss, the postural demand of transfemoral amputees increases which significantly modulates the spectral power of intrinsic cortical oscillations. Findings: Using the independent components from the sensorimotor areas and supplementary motor area (SMA), our results present a well-pronounced drop of alpha spectral power at sensorimotor area contralateral to sound limb of amputees in comparison to SMA and the sensorimotor area contralateral to prosthetic limb. Following this, we found significantly higher (p < 0.05) limit of stability (LOS) at their sound limb than at the prosthetic limb. Healthy individuals have similar contribution from both the limbs and the EEG alpha spectral power was similar across the three regions of the cortex during the balance control task as expected. Overall, a decent correlation was found between the LOS and alpha spectral power in both amputee and healthy individuals (Pearson’s correlation coefficient > 0.5). Interpretation: By externally stimulating the highlighted cortical regions, neuroplasticity might be promoted which helps to reduce the training time for the efficient rehabilitation of amputees. Additionally, this new knowledge might benefit in the designing and development of innovative interventions to prevent falls due to lower limb amputation.

6

A novel approach for detection of consciousness level in comatose patients from EEG signals with 1-D convolutional neural network

Altıntop Çiğdem Gülüzar, Latifoğlu Fatma, Akın Aynur Karayol, Çetin Bilge

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

16--26

EN

Coma is an unresponsive state of unconsciousness from which a person cannot be awakened. Glasgow Coma Score (GCS) is a clinical scale for determining the depth and length of a coma. GCS plays an important role in effective and accurate patient evaluation and is critical in planning the right treatment modalities and patient care because it shows patient outcomes and is a measurement performed several times a day. The GCS is universally accepted as a gold standard and validated scale for assessing a patient’s level of consciousness. However, the scale’s success has been questioned due to variations in interobserver reliability performance. In this study, the data set generated from Electroencephalography (EEG) signals obtained from 39 comatose patients was used in the training of deep neural networks for the classification of consciousness level. The EEG signals were recorded during nurse and family interaction with comatose patients. The level of consciousness was classified with the proposed 1D-CNN model. Consequently, the two classes that we label as low and high consciousness are classified with 83.3% accuracy. To our best knowledge, no prior studies are using 1D-CNN for the classification of EEG-based level of consciousness using the proposed recording process. Our study is unique from other studies in terms of recording procedure and methods.

7

Sequence-to-sequence deep neural network with spatio-spectro and temporal features for motor imagery classification

Rammy Sadaqat Ali, Abbas Waseem, Mahmood Syed Shahid, Riaz Haider, Rehman Haseeb Ur, Abideen Rao Zain Ul, Aqeel Muhammad, Zhang Wu

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

97--110

EN

Electroencephalography (EEG) is a method of the brain–computer interface (BCI) that measures brain activities. EEG is a method of (non-)invasive recording ofthe electrical activity ofthe brain. This can be used to build BCIs. From the last decade, EEG has grasped researchers' attention to distinguish human activities. However, temporal information has rarely been retained to incorporate temporal information for multi-class (more than two classes) motor imagery classification. This research proposes a long-short-term-memory-based deep learning model to learn the hidden sequential patterns. Two types of features are used to feed the proposed model, including Fourier Transform Energy Maps (FTEMs) and Common Spatial Patterns (CSPs) filters. Multiple experiments have been conducted on a publicly available dataset. Extraction of spatial and spectro-temporal features using CSP filters and FTEM allow the sequence-tosequence based proposed model to learn the hidden sequential features. The proposed method is trained, evaluated, and optimized for a publicly available benchmark data set and resulted in 0.81 mean kappa value. Obtained results depict the model robustness for the artifacts and suitable for real-life applications with comparable classification accuracy. The code and findings will be available at https://github.com/waseemabbaas/Motor-Imagery-Classification.git.

8

Detection of Parkinson’s disease using automated tunable Q wavelet transform technique with EEG signals

Khare Smith K., Bajaj Varun, Acharya U. Rajendra

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

679--689

EN

Deep brain simulations play an important role to study physiological and neuronal behavior during Parkinson’s disease (PD). Electroencephalogram (EEG) signals may faithfully represent the changes that occur during PD in the brain. But manual analysis of EEG signals is tedious, and time consuming as these signals are complex, non-linear, and non-stationary nature. Therefore EEG signals are required to decompose into multiple subbands (SBs) to get detailed and representative information from it. Experimental selection of basis function for the decomposition may cause system degradation due to information loss and an increased number of misclassification. To address this, an automated tunable Q wavelet transform (A-TQWT) is proposed for automatic decomposition. A-TQWT extracts representative SBs for analysis and provides better reconstruction for the synthesis of EEG signals by automatically selecting the tuning parameters. Five features are extracted from the SBs and classified different machine learning techniques. EEG dataset of 16 healthy controls (HC) and 15 PD (ON and OFF medication) subjects obtained from ”openneuro” is used to develop the automated model. We have aimed to develop an automated model that effectively classify HC subjects from PD patients with and without medication. The proposed method yielded an accuracy of 96.13% and 97.65% while the area under the curve of 97% and 98.56% for the classification of HC vs PD OFF medication and HC vs PD ON medication using least square support vector machine, respectively.

9

Automated detection of driver fatigue from electroencephalography through wavelet-based connectivity

Ahmadi Amirmasoud, Bazregarzadeh Hanieh, Kazemi Kamran

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

316--332

EN

Background: Mental fatigue is one of the most causes of road accidents. Identification of biological tools and methods such as electroencephalogram (EEG) are invaluable to detect them at early stage in hazard situations. Methods: In this paper, an expert automatic method based on brain region connectivity for detecting fatigue is proposed. The recorded general data during driving in both fatigue (the last five minutes) and alert (at the beginning of driving) states are used in analyzing the method. In this process, the EEG data during continuous driving in one to two hours are noted. The new feature of Gaussian Copula Mutual Information (GCMI) based on wavelet coefficients is calculated to detect brain region connectivity. Classification for each subject is then done through selected optimal features using the support vector machine (SVM) with linear kernel. Results: The designed technique can classify trials with 98.1% accuracy. The most significant contributions to the selected features are the wavelet coefficients details 1_2 (corresponding to the Beta and Gamma frequency bands) in the central and temporal regions. In this paper, a new algorithm for channel selection is introduced that has been able to achieve 97.2% efficiency by selecting eight channels from 30 recorded channels. Conclusion: The obtained results from the classification are compared with other methods, and it is proved that the proposed method accuracy is higher from others at a significant level. The technique is completely automatic, while the calculation load could be reduced remarkably through selecting the optimal channels implementing in real-time systems.

10

Artifact removal from EEG signals recorded in non-restricted environmentArtifact removal from EEG signals recorded in non-restricted environment

Jamil Zainab, Jamil Afshan, Majid Muhammad

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

503--515

EN

Electroencephalography (EEG) signals are always accompanied by endogenous and exogenous artifacts. Research carried out in the past few years focused on EEG artifact removal considered EEG signals recorded in a restricted lab environment. Considering the importance of EEG in daily life activities, no definitive approach is presented in removing blink artifacts from non-restricted EEG recordings. In this paper, a new supervised artifact removal method is proposed that classifies EEG chunks having eye movements and then utilizes independent component analysis and discrete wavelet transform to eliminate the ocular artifacts. The EEG data is acquired from 29 subjects in a non-restricted environment where the subject has to watch videos while walking and giving gestures and facial expressions. Thirteen morphological features are extracted from the recorded EEG signals to classify chunks with eye movements. The EEG chunks with eye movements are further processed to remove noise without distorting the morphology of signals. The proposed method is tested for eye movements and shows an improved performance in terms of correlation, mutual information, phase difference, and computational time over unsupervised modified multi-scale sample entropy and kurtosis, and wavelet enhanced independent component analysis based approaches. Moreover, the computed values of statistical parameters including sensitivity and specificity show the robustness of the proposed scheme.

11

Using brain-computer interface technology as a controller in video games

Zając Błażej, Paszkiel Szczepan

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2020

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T. 10, nr 3

26--31

EN

Nowadays, control in video games is based on the use of a mouse, keyboard and other controllers. A Brain Computer Interface (BCI) is a special interface that allows direct communication between the brain and the appropriate external device. Brain Computer Interface technology can be used forcommercial purposes, for example as a replacement for a keyboard,mouse or other controller. This article presents a method of controlling video games using the EMOTIV EPOC + Neuro Headset as a controller.

PL

W obecnych czasach sterowanie w grach wideo jest oparte na wykorzystaniu myszki, klawiatury oraz innych kontrolerów. Brain-Computer Interface w skrócie BCI to specjalnyinterfejspozwalający na bezpośrednią komunikację międzymózgiem,a odpowiednim urządzeniem zewnętrznym. Technologia Brain-Computer Interface może zostać użyta w celach komercyjnych na przykład jako zamiennik myszki klawiatury lub innego kontrolera. Wartykule przedstawiono sposób sterowania w grach wideo przy pomocy neuro-headsetu EMOTIV EPOC+ jako kontrolera.

12

Sleep EEG analysis utilizing inter-channel covariance matrices

Gopan K. Gopika, Prabhu Sathvik S., Sinha Neelam

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

527--545

EN

Background: Sleep is vital for normal body functions as sleep disorders can adversely affect a person. Electroencephalographic (EEG) signals indicate brain functions and have characteristic signatures for various sleep stages. These enable the use of EEG as an effective tool for in-depth studies about sleep. Sleep stages are broadly divided as rapid eye movement (REM) and non-rapid eye movement (NREM). NREM is further divided into 3 stages. The objective of the work is to distinguish the given EEG epoch as wake, NREM1, NREM2, NREM3 and REM. DREAMS Subject Database containing 5 EEG channels is used here. This work focuses on utilizing EEG by exploiting variations in inter-dependencies of different brain regions during sleep. New method: Covariance matrices of the wavelet-decomposed channels are used to obtain the variations in inter-dependencies. The feature sets are: (1) simple matrix properties(MF) like trace, determinant and norm, (2) eigen-values (E1), (3) eigen-vector corresponding to the largest eigen-value (E2) and (4) tangent vectors obtained using Riemann geometry (RG-TS). The features are input to ensemble classifier with bagging. Subject-specific, All-subjects-combined and Leave-one-subject-out methods of analysis are carried out. Results: In all methods of analysis, RG-TS features give maximum accuracy (80.05%, 83.05% and 61.79%), closely followed by E1 (79.49%, 77.14% and 58.34%). Comparison with existing method: The proposed method obtains higher and/or comparable accuracy. This work also ensures no biasing of classifier due to unequal class distribution. Conclusion: The performances of RG-TS and E1 features reveal that the changes in interdependencies of pre-frontal and occipital lobe along with the central lobe can be used to distinguish the different sleep stages.

13

Detection of SSVEP based on empirical mode decomposition and power spectrum peaks analysis

Antelis Javier M., Rivera Camilo A., Galvis Eduard, Ruiz-Olaya Andres F.

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1010--1021

EN

Steady-state visual evoked potential (SSVEP) based brain–computer interfaces have been widely studied because these systems have potential to restore capabilities of communication and control of disable people. Identifying target frequency using SSVEP signals is still a great challenge due to the poor signal-to-noise ratio of these signals. Commonly, this task is carried out with detection algorithms such as bank of frequency-selective filters and canonical correlation analysis. This work proposes a novel method for the detection of SSVEP that combines the empirical mode decomposition (EMD) and a power spectral peak analysis (PSPA). The proposed EMD+PSPA method was evaluated with two EEG datasets, and was compared with the widely used FB and CCA. The first dataset is freely available and consists of three flickering light sources; the second dataset was constructed and consists of six flickering light sources. The results showed that proposed method was able to detect SSVEP with high accuracy (93.67 ± 9.97 and 78.19 ± 23.20 for the two datasets). Furthermore, the detection accuracy results achieved with the first dataset showed that EMD+PSPA provided the highest detection accuracy (DA) in the largest number of participants (three out of five), and that the average DA across all participant was 93.67 ± 9.97 which is 7% and 4% more than the average DA achieved with FB and CCA, respectively.

14

Detection of eye closing/opening from EOG and its application in robotic arm control

Sharma Kamal, Jain Neeraj, Pal Prabir K.

Biocybernetics and Biomedical Engineering

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2020

|

Vol. 40, no. 1

173--186

EN

Detection of eye closing/opening from alpha-blocking in the EEG of occipital region has been used to build human-machine interfaces. This paper presents an alternative method for detection of eye closing/opening from EOG signals in an online setting. The accuracies for correct detection of eye closing and opening operations with the proposed techniques were found to be 95.6% and 91.9% respectively for 8 healthy subjects. These techniques were then combined with the detection of eye blinks, the accuracy of which turned out to be 96.9%. This was then used to build an interface for robotic arm control for a pick and place task. The same task was also carried out using a haptic device as a master. The speed and accuracy for these two methods were then compared to assess quantitatively the ease of using this interface. It appears that the proposed interface will be very useful for persons with neurodegenerative disorders who can perform eye closing/opening and eye blinks.

15

Comparison of different feature extraction methods for EEG-based emotion recognition

Nawaz Rab, Cheah Kit Hwa, Nisar Humaira, Yap Vooi Voon

Biocybernetics and Biomedical Engineering

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2020

|

Vol. 40, no. 3

910--926

EN

EEG-based emotion recognition is a challenging and active research area in affective computing. We used three-dimensional (arousal, valence and dominance) model of emotion to recognize the emotions induced by music videos. The participants watched a video (1 min long) while their EEG was recorded. The main objective of the study is to identify the features that can best discriminate the emotions. Power, entropy, fractal dimension, statistical features and wavelet energy are extracted from the EEG signals. The effects of these features are investigated and the best features are identified. The performance of the two feature selection methods, Relief based algorithm and principle component analysis (PCA), is compared. PCA is adopted because of its improved performance and the efficacies of the features are validated using support vector machine, K-nearest neighbors and decision tree classifiers. Our system achieves an overall best classification accuracy of 77.62%, 78.96% and 77.60% for valence, arousal and dominance respectively. Our results demonstrated that time-domain statistical characteristics of EEG signals can efficiently discriminate different emotional states. Also, the use of three-dimensional emotion model is able to classify similar emotions that were not correctly classified by two-dimensional model (e.g. anger and fear). The results of this study can be used to support the development of real-time EEG-based emotion recognition systems.

16

Classification of pilots' mental states using a multimodal deep learning network

Han Soo-Yeon, Kwak No-Sang, Oh Taegeun, Lee Seong-Whan

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

324--336

EN

An automation system for detecting the pilot's diversified mental states is an extremely important and essential technology, as it could prevent catastrophic accidents caused by the deteriorated cognitive state of pilots. Various types of biosignals have been employed to develop the system, since they accompany neurophysiological changes corresponding to the mental state transitions. In this study, we aimed to investigate the feasibility of a robust detection system of the pilot's mental states (i.e., distraction, workload, fatigue, and normal) based on multimodal biosignals (i.e., electroencephalogram, electrocardiogram, respiration, and electrodermal activity) and a multimodal deep learning (MDL) network. To do this, first, we constructed an experimental environment using a flight simulator in order to induce the different mental states and to collect the biosignals. Second, we designed the MDL architecture – which consists of a convolutional neural network and long short-term memory models – to efficiently combine the information of the different biosignals. Our experimental results successfully show that utilizing multimodal biosignals with the proposed MDL could significantly enhance the detection accuracy of the pilot's mental states.

17

Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform

George S. Thomas, Subathra M. S. P., Sairamya N. J., Susmitha L., Joel Premkumar M.

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 2

709--728

EN

Epilepsy is a widely spread neurological disorder caused due to the abnormal excessive neural activity which can be diagnosed by inspecting the electroencephalography (EEG) signals visually. The manual inspection of EEG signals is subjected to human error and is a tedious process. Further, an accurate diagnosis of generalized and focal epileptic seizures from normal EEG signals is vital for the supervision of pertinent treatment, life advancement of the subjects, and reduction in cost for the subjects. Hence the development of automatic detection of generalized and focal epileptic seizures from normal EEG signals is important. An approach based on tunable-Q wavelet transform (TQWT), entropies, Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) is proposed in this work for detection of epileptic seizures and its types. Two EEG databases namely, Karunya Institute of Technology and Sciences (KITS) EEG database and Temple University Hospital (TUH) database consisting of normal, generalized and focal EEG signals is used in this work to analyze the performance of the proposed approach. Initially, the EEG signals are decomposed into sub-bands using TQWT and the non-linear features like log energy entropy, Shannon entropy and Stein's unbiased risk estimate (SURE) entropy is computed from each sub-band. The informative features from the computed feature vectors are selected using PSO and fed into ANN for the classification of EEG signals. The proposed algorithm for KITS database achieved a maximum accuracy of 100% for four experimental cases namely, (i) normal-focal, (ii) normal-generalised, (iii) normal-focal + generalised and (iv) normal-focal-generalised. The TUH database achieved an accuracy of 95.1%, 97.4%, 96.2% and 88.8% for the four experimental cases. The proposed approach is promising and able to discriminate the epileptic seizure types with satisfactory classification performance.

18

A robust method for early diagnosis of autism spectrum disorder from EEG signals based on feature selection and DBSCAN method

Abdolzadegan Donya, Moattar Mohammad Hossein, Ghoshuni Majid

Biocybernetics and Biomedical Engineering

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2020

|

Vol. 40, no. 1

482--493

EN

Electroencephalogram (EEG) is one of the most important signals for diagnosis of Autism Spectrum Disorder (ASD). There are different challenges such as feature selection and the existence of artifacts in EEG signals. This article aims to present a robust method for early diagnosis of ASD from EEG signal. The study population consists of 34 children with ASD between 3–12 years and 11 healthy children in the same ranges of age. The proposed approach uses linear and nonlinear features such as Power Spectrum, Wavelet Transform, Fast Fourier Transform (FFT), Fractal Dimension, Correlation Dimension, Lyapunov Exponent, Entropy, Detrended Fluctuation Analysis and Synchronization Likelihood for describing the EEG signal. In addition Density Based Clustering is utilized for artifact removal and robustness. Besides, features selection is applied based on different criterions such as Mutual Information (MI), Information Gain (IG), Minimum-Redundancy Maximum-Relevancy (mRmR) and Genetic Algorithm (GA). Finally, the K-Nearest-Neighbor (KNN) and Support Vector Machines (SVM) classifiers are used for final decision. As a result, the investigation indicates that the classification accuracy of the approach using SVM is 90.57% while for KNN it is 72.77%. Moreover, the sensitivity of the proposed method is 99.91% for SVM and 91.96% for KNN. Also, experiments show that DFA, LE, Entropy and SL features have considerable influence in promoting the classification accuracy.

19

A comparative analysis of signal processing and classification methods for different applications based on EEG signals

Khosla Ashima, Khandnor Padmavati, Chand Trilok

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 2

649--690

EN

Electroencephalogram (EEG) measures the neuronal activities in the form of electric currents that are generated due to the synchronized activity by a group of specialized pyramidal cells inside the brain. The study presents a brief comparison of various functional neuroimaging techniques, revealing the excellent neuroimaging capabilities of EEG signals such as high temporal resolution, inexpensiveness, portability, and non-invasiveness as compared to the other techniques such as positron emission tomography, magnetoencephalogram, func-tional magnetic resonance imaging, and transcranial magnetic stimulation. Different types of frequency bands associated with the brain signals are also being summarized. The main purpose of this literature survey is to cover the maximum possible applications of EEG signals based on computer-aided technologies, ranging from the diagnosis of various neurological disorders such as epilepsy, major depressive disorder, alcohol use disorder, and dementia to the monitoring of other applications such as motor imagery, identity authentication, emotion recognition, sleep stage classification, eye state detection, and drowsiness monitoring. After reviewing them, the comparative analysis of the publicly available EEG datasets and other local data acquisition methods, preprocessing techniques, feature extraction methods, and the result analysis through the classification models and statistical tests has been presented. Then the research gaps and future directions in the present studies have been summarized with the aim to inspire the readers to explore more opportunities on the current topic. Finally, the survey has been completed with the brief description about the studies exploring the fusion of brain signals from multiple modalities.

20

Improved robust weighted averaging for event-related potentials in EEG

Kotowski Krzysztof, Stapor Katarzyna, Leski Jacek

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 4

1036--1046

EN

The aim of this study was to improve the robust weighted averaging based on criterion function minimization and assess its effectiveness for extracting event-related brain potentials (ERP) from electroencephalographic (EEG) recordings. The areas of improvement include significantly lower averaging error (45% lower RMSE and 37% lower maximum difference than for original implementation) and increased robustness to local minima, strong outliers and corrupted epochs common to real-life EEG signals, especially from low-cost devices. Our proposed procedure was tested on two datasets, one artificially generated for purposes of this study (including different noise sources) and one real-life dataset collected with Emotiv EPOCþ. The lower error results mainly from more effective rejection (lowering the weights) of corrupted epochs by integrating the correlation-based weighting. The advantages of our method over pure correlation-based weighting are lower RMSE (up to two times) and robustness to the algorithm initialization and strong outliers. The performance of the methods was measured using bootstrap testing to avoid dependency of results on data. It shows that our improvements lead to significantly lower error, especially when the EEG signal is not filtered. The values of the parameters were adjusted for EEG signals but they can easily be incorporated in other repetitive electrophysiological measurement techniques.