Independent Component Analysis for EEG Data Preprocessing - Algorithms Comparison

Rejer, Izabela; Górski, Paweł

doi:10.1007/978-3-642-40925-7_11

Cited by 18 publications

(9 citation statements)

References 27 publications

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“…Algorithm [25]. There are also many feature extraction and selection algorithms, such as covariance matrix [26] and the tensor method [27].…”

Section: General Bcimentioning

confidence: 99%

“…The personalized processing of brain signals includes preprocessing, feature extraction and selection, and classification. The classical algorithms for brain signal preprocessing include Kalman filtering [ 24 ] and the Independent Component Correlation Algorithm [ 25 ]. There are also many feature extraction and selection algorithms, such as covariance matrix [ 26 ] and the tensor method [ 27 ].…”

Section: Personalized Bcimentioning

confidence: 99%

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Personalized Brain–Computer Interface and Its Applications

Gong

Nan

et al. 2022

JPM

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Brain–computer interfaces (BCIs) are a new technology that subverts traditional human–computer interaction, where the control signal source comes directly from the user’s brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.

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“…Algorithm [25]. There are also many feature extraction and selection algorithms, such as covariance matrix [26] and the tensor method [27].…”

Section: General Bcimentioning

confidence: 99%

Section: Personalized Bcimentioning

confidence: 99%

Personalized Brain–Computer Interface and Its Applications

Gong

Nan

et al. 2022

JPM

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“…spatial filters, frequency filters, Independent Component Analysis etc. [1,2,3]). To address the second problem the methods for feature selection have to be applied (e.g.…”

Section: Introductionmentioning

confidence: 99%

Classic genetic algorithm vs. genetic algorithm with aggressive mutation for feature selection for a brain-computer interface

Rejer

Lorenz

2015

ELECTROTECHNICAL REVIEW

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The classic genetic algorithm has been successfully applied to many optimization problems. However, its usefulness is limited when it comes to feature selection, particularly if a high reduction rate is expected. The algorithm, in its classic version, returns feature sets containing approximately 50% of the total number of features. In order to decrease this rate, a penalty term penalizing individuals of too many features is often added to the fitness function. This solution seems to be reasonable but, as will be shown in this paper, provides only a slight improvement in the reduction rate. In order to obtain a satisfactory classification accuracy and a high reduction rate, not only the fitness function but also other algorithm elements must be reconsidered. Streszczenie. Klasyczny algorytm genetyczny był z powodzeniem stosowany w wielu problemach optymalizacyjnych, jednakże jego użyteczność jest ograniczona w problemach selekcji cech, zwłaszcza jeżeli wymagana jest wysoka stopa redukcji cech. Algorytm, w jego klasycznej wersji, zwraca zbiory cech zawierające około 50% pierwotnej liczby cech. W celu zmniejszenia tej liczby, do funkcji przystosowania algorytmu dołącza się często człon kary, karzący osobniki kodujące zbiory o zbyt dużej liczbie cech. Takie rozwiązanie wydaje się być rozsądne, ale, jak zostanie to przedstawione w artykule pozwala jedynie na niewielką poprawę stopy redukcji. Stąd, w celu uzyskania satysfakcjonującej dokładności klasyfikacji i wysokiej stopy redukcji, nie tylko funkcja przystosowania, ale również inne elementy algorytmu muszą zostać wzięte pod uwagę (Klasyczny algorytm genetyczny, a algorytm z agresywną mutacją w procesie selekcji cech na potrzeby interfejsu mózg-komputer).

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“…Over the past decade, independent component analysis (ICA) algorithms are used extensively for feature extraction in speech signals6‐9 and in biomedical engineering for electroencephalogram (EEG) signal analysis to remove artifacts from the signal 10‐12. More recently, ICA is applied to improve the channel estimation accuracy in the orthogonal frequency‐division multiplexing (OFDM) systems 13.…”

Section: Introductionmentioning

confidence: 99%

Implementation of fast independent component analysis on field‐programmable gate array for resolving the slot collision issue in the space‐based automatic identification system

KrishnaAdithya

NimbalAkshay

MakamAdarsh

et al. 2020

Satell Commun Network

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The space-based automatic identification system (S-AIS) is a maritime safety and vessel monitoring system used to broadcast the ship position and other telemetry every few seconds in short bursts. A satellite-based AIS receiver is of interest to defense community as it provides global or wide-area coverage. However, when the satellite passes regions with elevated traffic density, a major challenge is to overcome the message or signal interference at the satellite receiver. This paper mainly focuses on developing a novel AIS satellite receiver that uses fast independent component analysis (FastICA) blind source separation technique to resolve the overlapped signals at the satellite. We propose an architecture to realize FastICA algorithm on hardware. In addition to this, we present Gaussian minimum shift keying modulator and demodulator design schemes used by the S-AIS transponders. The proposed framework is realized on Virtex5-xc5vlx110t field-programmable gate array. The results obtained validate the effectiveness of the FastICA architecture realized by recovering the original ship data from the overlapped signals. K E Y W O R D S blind source separation (BSS), field programmable gate array (FPGA), Gaussian minimum shift keying (GMSK), space-based automatic identification system (S-AIS) 1 | INTRODUCTION The automatic identification system is formulated to provide the static and dynamic ship information to other vessels and the base station for monitoring the ships, for handling the traffic flow, and to prevent the accidental collisions between the ships in high seas. The static information includes call sign, International Maritime Organization (IMO) number, and type of ship, and dynamic information comprises time in Coordinated Universal Time (UTC), speed over ground, heading, and navigational status.1 The conventional communication between the ship to shore and ship to ship in the ground-based network is limited in range by horizon to 40 nautical miles. This constrain is overcome by space-based automatic identification system (AIS), wherein the messages from the ships are received by the satellite for providing global coverage. The transmissions are made with the Gaussian minimum shift keying (GMSK) modulation technique with data rate of 9600 bps in the very high frequency (VHF) maritime mobile band over the frequencies channels 161.975 and 162.025 MHz. Self-organized time-division multiple access (SOTDMA) is the multiple access scheme employed that works on 2250 time slots with the slot duration of 26.66 ms established every 60 s.2 The slot allocation is self-organized within a structured area that is termed as AIS cells. However, the field of view (FoV) of the satellite spans a large number of self organized area (AIS cells). Hence, when several AIS cells are covered during the satellite visibility period, AIS messages sent in the same time slot from different organized areas will reach satellite simultaneously colliding with each other.3 Further AIS messages sent in

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Independent Component Analysis for EEG Data Preprocessing - Algorithms Comparison

Cited by 18 publications

References 27 publications

Personalized Brain–Computer Interface and Its Applications

Personalized Brain–Computer Interface and Its Applications

Classic genetic algorithm vs. genetic algorithm with aggressive mutation for feature selection for a brain-computer interface

Implementation of fast independent component analysis on field‐programmable gate array for resolving the slot collision issue in the space‐based automatic identification system

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