Band-sensitive seizure onset detection via CSP-enhanced EEG features

Qaraqe, Marwa; Ismail, Muhammad; Serpedin, Erchin

doi:10.1016/j.yebeh.2015.06.002

Cited by 32 publications

(21 citation statements)

References 40 publications

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“…Each selected time period was analyzed using time segments of 200 ms. Common spatial pattern (CSP) is widely used and highly successful in the binary case [33], which was applied in this study to perform feature extraction. Given two distributions in a high-dimensional space, the CSP algorithm can calculate spatial filters that maximize the variance between two classes [34]. For the analysis, let V denote the raw data of a single trial, an

N \times T

matrix with N the number of channels (28 channels in this study) and T the number of samples in time.…”

Section: Methodsmentioning

confidence: 99%

A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control

Tang

Sun

Zhang

et al. 2016

Sensors

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To recognize the user’s motion intention, brain-machine interfaces (BMI) usually decode movements from cortical activity to control exoskeletons and neuroprostheses for daily activities. The aim of this paper is to investigate whether self-induced variations of the electroencephalogram (EEG) can be useful as control signals for an upper-limb exoskeleton developed by us. A BMI based on event-related desynchronization/synchronization (ERD/ERS) is proposed. In the decoder-training phase, we investigate the offline classification performance of left versus right hand and left hand versus both feet by using motor execution (ME) or motor imagery (MI). The results indicate that the accuracies of ME sessions are higher than those of MI sessions, and left hand versus both feet paradigm achieves a better classification performance, which would be used in the online-control phase. In the online-control phase, the trained decoder is tested in two scenarios (wearing or without wearing the exoskeleton). The MI and ME sessions wearing the exoskeleton achieve mean classification accuracy of 84.29% ± 2.11% and 87.37% ± 3.06%, respectively. The present study demonstrates that the proposed BMI is effective to control the upper-limb exoskeleton, and provides a practical method by non-invasive EEG signal associated with human natural behavior for clinical applications.

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N \times T

matrix with N the number of channels (28 channels in this study) and T the number of samples in time.…”

Section: Methodsmentioning

confidence: 99%

A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control

Tang

Sun

Zhang

et al. 2016

Sensors

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“…We also report latency (time delay) between the annotated seizure onset and the detection by the method in Table 3. We compare classification accuracy and latency with the state-ofthe-art methods [13,26,23], which also report classification performance and latency for the Children's Hospital Boston database. We emphasise again that these state-of-the-art methods are significantly more computationally expensive than the proposed method.…”

Section: Resultsmentioning

confidence: 99%

“…Most methods use classification techniques from the supervised machine learning literature, such as support vector machines [10][11][12] and discriminant analysis [13], and differ mainly in terms of their feature extraction methods and the features classification approaches. Many methods use time-frequency descriptors, either explicitly (e.g., short-term Fourier or wavelet representations) [14][15][16][17]11,18,19], empirical mode decomposition [20][21][22], or implicitly by learning neural networks [23][24][25] or by using component analysis or common spatial patterns (see for example [26][27][28]). Some also use statistical descriptors such as signal entropy [17,11,[29][30][31] or fractal dimension [32,33].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, state-of-the-art detection methods cannot be incorporated into EEG devices to perform detection in real time. For example, the method [26] uses common spatial patterns that require estimating covariance matrices and performing singular value decompositions at each detection step. This limitation is motivating the development of detection methods that use cloud computing technology to perform detection on a high performance computing server that is accessed remotely (see for example [28]).…”

Section: Introductionmentioning

confidence: 99%

“…Another limitation of state-of-the-art methods is that they pull information from all spectral bands to improve detection performance [26]. While beneficial in terms of classification accuracy, this can be problematic in many clinical applications where the current practice is to detect seizures independently in each physiological spectral band or brain rhythm (these bands are specified in Section 2).…”

Section: Introductionmentioning

confidence: 99%

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Fast statistical model-based classification of epileptic EEG signals

Quintero-Rincón

Pereyra

D’Giano

et al. 2018

Biocybernetics and Biomedical Engineering

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This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the current medical practices; and it can be trained with small datasets, which is key in clinical problems where there is limited annotated data available. This is in sharp contrast with modern approaches based on machine learning techniques, which achieve very high sensitivity and specificity but require large training sets with expert annotations that may not be available. The proposed method proceeds by first separating EEG signals into their five brain rhythms by using a wavelet filter bank. Each brain rhythm signal is then mapped to a low-dimensional manifold by using a generalized Gaussian statistical model; this dimensionality reduction step is computationally straightforward and greatly improves supervised classification performance in problems with little training data available. Finally, this is followed by parallel linear classifications on the statistical manifold to detect if the signals exhibit healthy or abnormal brain activity in each spectral band. The good performance of the proposed method is demonstrated with an application to paediatric neurology using 39 EEG recordings from the Children's Hospital Boston database, where it achieves an average sensitivity of 98%, specificity of 88%, and detection latency of 4 s, performing similarly to the best approaches from the literature.

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Brain–Computer Interface Using Electroencephalographic Signals for the Internet of Robotic Things

Sudharsan¹,

Deny²

2021

Human Communication Technology

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Band-sensitive seizure onset detection via CSP-enhanced EEG features

Cited by 32 publications

References 40 publications

A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control

A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control

Fast statistical model-based classification of epileptic EEG signals

Brain–Computer Interface Using Electroencephalographic Signals for the Internet of Robotic Things

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