Papers by Andrzej Majkowski
Praktyczne aspekty analizy widmowej Fouriera (Encyklopedia Przeglądu Elektrotechnicznego)
Przegląd Elektrotechniczny, 2004
Neurofeedback - eksperymenty w LabVIEW
Przegląd Elektrotechniczny, 2013
Sieci neuronowe i transformacja falkowa w zastosowaniu do kompresji sygnałów pomiarowych - analiza porównawcza
Fatigue Detection Caused by Office Work With the Use of EOG Signal
IEEE Sensors Journal, 2020
Although the psychophysiological signs of fatigue are well known, automatic methods for the detec... more Although the psychophysiological signs of fatigue are well known, automatic methods for the detection of fatigue in employees in specific working conditions are still lacking. Many people do repetitive work on computers and become fatigued; therefore, the detection of fatigue in employees can help prevent accidents and increase their work efficiency. In this article, we propose an algorithm for the effective detection of fatigue which is based only on electrooculographic (EOG) signal. Three features were assessed: blink duration, blink amplitude, and time between blinks. To cause fatigue, the ${N}$ -back test, lasting for 60 minutes, was carried out. The article presents the research results for 24 users. The effectiveness of the proposed system was measured by the accuracy of classification. The average classification accuracy was 0.93 for user-dependent mode and 0.89 for user-independent mode. The results of the conducted experiments indicated that assessing the three proposed features can help in the effective detection of fatigue in users.
Identification of Gender Based on Speech Signal
2019 IEEE 20th International Conference on Computational Problems of Electrical Engineering (CPEE)
The article presents a gender identification based on speech signal with supervised machine learn... more The article presents a gender identification based on speech signal with supervised machine learning implementation. At first, a database of speech signals in Polish language was collected. Next, a set of features from audio signal were calculated. The features were farther used to train a neural network. Audio signal processing and implementation of the neural network were performed in Python, and the calculation of features in the R language. Neural network training process was carried out using only CPU, then CPU with GPU and the times of the programs execution were compared. The obtained accuracy of gender recognition was 92.4%. The use of GPU accelerated the network learning process several times.
Using deep learning to recognize the sign alphabet
PRZEGLĄD ELEKTROTECHNICZNY
This article describes a vision system that uses deep learning to recognize 24 static signs of th... more This article describes a vision system that uses deep learning to recognize 24 static signs of the American Sign Alphabet in real time. As part of the project, images of signs from four publicly available databases were used as a training set. A DenseNet was implemented for image recognition. For testing, images were acquired with the use of a web camera. The accuracy of sign recognition in images is more than 80%. The real-time version of the system was implemented.
Detecting symptoms of driver fatigue using video analysis
19th International Conference Computational Problems of Electrical Engineering, 2018
The article describes a system of fatigue symptoms detection of a driver, based on his behavior o... more The article describes a system of fatigue symptoms detection of a driver, based on his behavior observed with a camera. The software was written in C++. Selected functions from OpenCV and Dlib libraries were used. We analyzed the following symptoms indicating driver fatigue: blinking, yawning, turning the head, falling head forward and to the side. Experiments were performed using YawDD database. Satisfactory effectiveness of fatigue symptoms detection was achieved. The effectiveness of blink detection was 61%. For the rest of symptoms the detection accuracy was about 86%.
Automatic Traffic Monitoring Using Images from Road Camera
2020 IEEE 21st International Conference on Computational Problems of Electrical Engineering (CPEE), 2020
The article presents an algorithm for visual inspection of traffic intensity. At first, the acqui... more The article presents an algorithm for visual inspection of traffic intensity. At first, the acquisition process of video material from a road camera is described. Then the algorithm for processing and analyzing images from the recorded video material is presented. Software was prepared in MATLAB environment. Algorithm tests were conducted in real conditions, at different times of the day, different atmospheric conditions and different levels of traffic intensity. Test results show that in good working conditions the vehicle counting accuracy was 95.6%. When the sun shined in the camera lens the counting accuracy decreased to 87.2%. The smallest accuracy 83.2% was noted for traffic jams.
Dataset BCI EEG SSVEP for four classes of stimuli
Experimental Setup Five users, at the age of 23, 25 31, 42, and 46 participated in the experiment... more Experimental Setup Five users, at the age of 23, 25 31, 42, and 46 participated in the experiment. Users sat comfortably in a chair. A green LED of a 1cm diameter was placed at a distance of about 1 meter from the eyes of a person. EEG signals were recorded using g.USBAmp with 16 active electrodes. Users were stimulated with flickering LED light of frequencies: 5Hz, 6Hz, 7Hz and 8Hz. The stimulation lasted 30 seconds. All sessions took place at the same time of the day to avoid circadian influences on the measurements. The electrodes were placed according to the international 10-20 system at positions: O2, AF3, AF4, P4, P3, F4, Fz, F3, FCz, Pz, C4, C3, CPz, Cz, Oz, O1. EEG sampling frequency was 256Hz. The signals were recorded using a Butterworth bandpass filter (0.1-100Hz) and notch filter (48-52Hz) to correct a technical artifact from the power network. Format of the Data For every user there are 4 files (for the stimuli of 5Hz, 6Hz, 7Hz and 8Hz each). Data are provided in Matlab format (*.mat) as X variable containing raw EEG signals: the 16 EEG potentials acquired in order: O2, AF3, AF4, P4, P3, F4, Fz, F3, FCz, Pz, C4, C3, CPz, Cz, Oz, O1.
Implementation of Lagged Phase Space for Spike Detection
2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2018
The presurgical evaluation patients for resective epilepsy surgery require localization of the ep... more The presurgical evaluation patients for resective epilepsy surgery require localization of the epileptogenic cortical zone (EZ). The detection and analysis of interictal and ictal epileptiform spikes is of major importance for identifying this area. The “irritative zone” of cortex with interictal spikes are usually revealed intraoperatively during acute electrocorticogram (ECoG). Since ECoG recordings cannot be completely visually reviewed in a reasonable amount of time, computer algorithms for automatic detection of seizures and spikes were developed. In this article we present a method of spike detection in ECoG signal using lagged phase space (LPS). Vectors in lagged phased space are treated as features. For spike detection we used expectation-maximization (EM) clustering algorithm. In this way we obtained quite high detection accuracy 96.4%.
2017 18th International Conference on Computational Problems of Electrical Engineering (CPEE), 2017
Для цитирования: Яковлев П.П. Активность ароматазы Р450 яичников в естественном менструальном цик... more Для цитирования: Яковлев П.П. Активность ароматазы Р450 яичников в естественном менструальном цикле и при стимуляции суперовуляции // Журнал акушерства и женских болезней.
Selection of EEG signal features for ERD/ERS classification using genetic algorithms
2017 18th International Conference on Computational Problems of Electrical Engineering (CPEE), 2017
The article presents the use of genetic algorithm (GA) to select and classify ERD/ERS patterns. O... more The article presents the use of genetic algorithm (GA) to select and classify ERD/ERS patterns. One hundred twenty eight channel EEG signal was used in the experiments. The signal was recorded for 40 people, during the process of imagining right and left hand movements. Feature extraction was performed using frequency analysis (FFT) with the resolution of 1Hz. So the features were spectral lines associated with particular electrodes. The selection of features, calculated for all people, was made with GA. The fitness function used in GA was EEG signal classification error calculated using LDA classifier and 5-CV test. The average accuracy of the classification for all people in 8–30Hz band was 0.85, while for the top 10 results 0.92.
Processing and Analysis of EEG Signal for SSVEP Detection
The aim of the article is to provide a systematic presentation of basic tools that are most commo... more The aim of the article is to provide a systematic presentation of basic tools that are most commonly used to analyze electroencephalography signals (EEG) in brain–computer interfaces for detection of steady-state visually evoked potentials (SSVEP). We use a database of EEG signals containing SSVEP and demonstrate the desirability of the use of selected methods, showing their benefits. Methods such as independent components analysis (ICA), frequency analysis (DFT), and time-frequency analysis (STFT) are presented. For SSVEP, the features of EEG signal should be stable with time. Short-Time Fourier Transform (STFT) allows to confirm this stability. Independent Component Analysis is used to extract pure SSVEP components. The advantages of each method are described and the obtained results are discussed. Further, source location by the use of low-resolution electromagnetic tomography algorithm is demonstrated.
PRZEGLĄD ELEKTROTECHNICZNY, 2020
An anti-G straining maneuver (AGSM) is an essential element of training pilots of high-maneuver a... more An anti-G straining maneuver (AGSM) is an essential element of training pilots of high-maneuver aircrafts. Electroencephalographic signal (EEG) registered during such maneuvers could be used to detect cerebral ischemia. AGSM involves complicated physical tasks, from stretching certain parts of muscles through adequate breathing. This results in the creation of extremely large muscle artefacts, which significantly disrupt the recorded EEG signals. The presented research concerned EEG signals, recorded during individual AGSM phases, inside an overload centrifuge. The largest artefacts in the EEG band (0.5-300Hz) were observed for the electrodes Fp1, F9, FT9 and EMG1 located on cheek. The signal from the Cz and Pz electrodes appeared to be the least disturbed. Streszczenie. Manewr przeciw-przeciążeniowy (AGSM) jest niezbędnym elementem szkolenia pilotów samolotów wysokomanewrowych. Sygnał elektroencefalograficzny (EEG) zarejestrowany podczas tych manewrów mógłby posłużyć do wykrycia niedokrwienia mózgu. Manewr przeciwprzeciążeniowy, obejmuje skomplikowane zadania fizyczne, od napinania pewnych partii mięśni, poprzez odpowiednie oddychanie. Powoduje to powstanie ekstremalnie dużych artefaktów odmięśniowych, które zakłócają, w sposób znaczący, rejestrowane sygnały EEG. Zaprezentowane badania dotyczyły sygnałów EEG zarejestrowanych podczas wykonywania poszczególnych faz AGSM, we wnętrzu wirówki przeciążeniowej. Największe artefakty w paśmie EEG (0.5-300Hz) zaobserwowano dla elektrod Fp1, F9, FT9 oraz EMG1 ulokowanej na policzku. Najmniej zakłócony okazał się sygnał zarejestrowany z elektrod Cz i Pz. (Analiza artefaktów w sygnale EEG zarejestrowanym w trakcie wykonywania manewru przeciw-przeciążeniowego)
Metrology and Measurement Systems, 2012
In the last decade of the XX-th century, several academic centers have launched intensive researc... more In the last decade of the XX-th century, several academic centers have launched intensive research programs on the brain-computer interface (BCI). The current state of research allows to use certain properties of electromagnetic waves (brain activity) produced by brain neurons, measured using electroencephalographic techniques (EEG recording involves reading from electrodes attached to the scalp-the non-invasive methodor with electrodes implanted directly into the cerebral cortex-the invasive method). A BCI system reads the user's "intentions" by decoding certain features of the EEG signal. Those features are then classified and "translated" (on-line) into commands used to control a computer, prosthesis, wheelchair or other device. In this article, the authors try to show that the BCI is a typical example of a measurement and control unit.
Procedia Computer Science, 2017
In the article there are presented the results of recognition of seven emotional states (neutral,... more In the article there are presented the results of recognition of seven emotional states (neutral, joy, sadness, surprise, anger, fear, disgust) based on facial expressions. Coefficients describing elements of facial expressions, registered for six subjects, were used as features. The features have been calculated for three-dimensional face model. The classification of features were performed using k-NN classifier and MLP neural network.
PRZEGLĄD ELEKTROTECHNICZNY, 2016
This article contains a description of a data acquisition system that enables simultaneous record... more This article contains a description of a data acquisition system that enables simultaneous recording of selected human physiological signals, resulting from brain electrical activity, eye movement, facial expression and skin-galvanic reaction. The signals, recorded using various types of sensors/devices, are fully synchronized and can be used to detect and identify emotions. Streszczenie. W artykule zamieszczono opis autorskiego stanowiska badawczego umożliwiającego równoczesną rejestrację wybranych sygnałów fizjologicznych człowieka, powstałych w efekcie elektrycznej aktywności mózgu, ruchu gałek ocznych, mimiki twarzy oraz reakcji skórnogalwanicznej. Sygnały zarejestrowane z użyciem różnego typu czujników/urządzeń są ze sobą w pełni zsynchronizowane i mogą być wykorzystane do wykrywania i rozpoznawania emocji. (Stanowisko badawcze do rejestracji wybranych sygnałów fizjologicznych na użytek rozpoznawania emocji).
BCI systems analyze the EEG signal and translate patient intentions into simple commands. Signal ... more BCI systems analyze the EEG signal and translate patient intentions into simple commands. Signal processing methods are very important in such systems. Signal processing covers: preprocessing, feature extraction, feature selection and classification. In the article authors present the results of implementing linear discriminant analysis as a feature reduction technique for BCI systems. Streszczenie: Systemy BCI analizują sygnał EEG i tłumaczą intencje użytkownika na proste polecenia. Ważnym elementem systemów BCI jest przetwarzanie sygnału. Obejmuje ono: przetwarzanie wstępne, ekstrakcję cech, selekcję cech i klasyfikację. W artykule autorzy prezentują wyniki badań z zastosowaniem liniowej analizy dyskryminacyjnej jako narzędzia do redukcji cech. (Liniowa analiza dyskryminacyjna jako narzędzie redukcji cech sygnału EEG)
artykule przedstawiono opracowaną przez autorów nową metodĊ ekstrakcji cech z sygnaáu EEG na uĪyt... more artykule przedstawiono opracowaną przez autorów nową metodĊ ekstrakcji cech z sygnaáu EEG na uĪytek interfejsów mózgkomputer (BCI). W opracowanych algorytmach ekstrakcji cech wykorzystano transformacjĊ falkową oraz statystyki wyĪszych rzĊdów. Przedstawiono wyniki badaĔ związanych z wykorzystaniem proponowanych metod ekstrakcji cech do konstrukcji interfejsu mózg-komputer dziaáającego w oparciu o wyobraĪanie sobie ruchu. Eksperymenty przeprowadzono przy uĪyciu dwóch elektrod (Nowa metoda ekstrakcji cech sygnaáu EEG na uĪytek interfejsów mózg-komputer).
The main goal of the article is to apply genetic algorithms to feature selection for the use of b... more The main goal of the article is to apply genetic algorithms to feature selection for the use of brain-computer interface (BCI). FFT coefficients of EEG signal were used as features. The best features for a BCI system depends on the person who uses the system as well as on the mental state of the person. Therefore, it is very important to apply efficient methods of feature selection. The genetic algorithm proposed by authors enables to choose the most representative features and electrodes.
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Papers by Andrzej Majkowski