Spectral Subtraction Denoising Preprocessing Block to Improve Slow Cortical Potential Based Brain–Computer Interface

Makary, Meena M.; Bu-Omer, Hani M.; Soliman, Ramy S.; Park, Kyungmo; Kadah, Yasser M.

doi:10.1007/s40846-017-0308-3

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…There are some widely used EEG signals in BCI applications like steady-state visual evoked potential (SSVEP) 6 , which are brain reactions to visual stimuli at some particular frequencies, and slow cortical potential (SCP) 7 , that are more associated with movement functions. Also, evoked potential P300 8 signal that has been commonly used as spellers, and motor imagery (MI) 9 .…”

Section: Introductionmentioning

confidence: 99%

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals

Salimpour

Kalbkhani

Seyyedi

et al. 2022

Sci Rep

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Over the past few years, the processing of motor imagery (MI) electroencephalography (EEG) signals has been attracted for developing brain-computer interface (BCI) applications, since feature extraction and classification of these signals are extremely difficult due to the inherent complexity and tendency to artifact properties of them. The BCI systems can provide a direct interaction pathway/channel between the brain and a peripheral device, hence the MI EEG-based BCI systems seem crucial to control external devices for patients suffering from motor disabilities. The current study presents a semi-supervised model based on three-stage feature extraction and machine learning algorithms for MI EEG signal classification in order to improve the classification accuracy with smaller number of deep features for distinguishing right- and left-hand MI tasks. Stockwell transform is employed at the first phase of the proposed feature extraction method to generate two-dimensional time–frequency maps (TFMs) from one-dimensional EEG signals. Next, the convolutional neural network (CNN) is applied to find deep feature sets from TFMs. Then, the semi-supervised discriminant analysis (SDA) is utilized to minimize the number of descriptors. Finally, the performance of five classifiers, including support vector machine, discriminant analysis, k-nearest neighbor, decision tree, random forest, and the fusion of them are compared. The hyperparameters of SDA and mentioned classifiers are optimized by Bayesian optimization to maximize the accuracy. The presented model is validated using BCI competition II dataset III and BCI competition IV dataset 2b. The performance metrics of the proposed method indicate its efficiency for classifying MI EEG signals.

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Section: Introductionmentioning

confidence: 99%

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals

Salimpour

Kalbkhani

Seyyedi

et al. 2022

Sci Rep

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“…After detection of BCI markers, classi cation algorithms are used to translate brain wave variations into machine command. The EEG features used most frequently by different research groups are broadly classi ed as self-regulated features consisting of slow cortical potential (SCP) [1][2][3], eventrelated synchronization (ERS) and event-related de-synchronization (ERD) [4,5], as well as evoked potentials such as P300 [6] and steady-state visually evoked potential (SSVEP) [7]. P300, which is an event-related potential (ERP), is represented by a positive amplitude shift in the EEG signal delayed by a period of 300 ms from the advent of an infrequent stimulus.…”

Section: Introductionmentioning

confidence: 99%

Synchronized Detection of Evoked Potentials to Drive a High Information Transfer Rate Hybrid Brain-Computer Interface Application

Katyal

Singla

2021

ABE

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“…These signals are considered stochastic because they have great variability and a low signal-to-noise ratio. At present, several types of EEG signals have been classified, such as the sensorimotor rhythm (SMR) [ 9 ], slow cortical potential (SCP) [ 10 ], event-related potential (ERP) [ 11 ], and steady-state visual evoked potential (SSVEP) [ 12 ], among others.…”

Section: Introductionmentioning

confidence: 99%

Use of the Stockwell Transform in the Detection of P300 Evoked Potentials with Low-Cost Brain Sensors

Vidal

Beltrán

Martínez-Sibaja

et al. 2018

Sensors

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The evoked potential is a neuronal activity that originates when a stimulus is presented. To achieve its detection, various techniques of brain signal processing can be used. One of the most studied evoked potentials is the P300 brain wave, which usually appears between 300 and 500 ms after the stimulus. Currently, the detection of P300 evoked potentials is of great importance due to its unique properties that allow the development of applications such as spellers, lie detectors, and diagnosis of psychiatric disorders. The present study was developed to demonstrate the usefulness of the Stockwell transform in the process of identifying P300 evoked potentials using a low-cost electroencephalography (EEG) device with only two brain sensors. The acquisition of signals was carried out using the Emotiv EPOC® device—a wireless EEG headset. In the feature extraction, the Stockwell transform was used to obtain time-frequency information. The algorithms of linear discriminant analysis and a support vector machine were used in the classification process. The experiments were carried out with 10 participants; men with an average age of 25.3 years in good health. In general, a good performance (75–92%) was obtained in identifying P300 evoked potentials.

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Spectral Subtraction Denoising Preprocessing Block to Improve Slow Cortical Potential Based Brain–Computer Interface

Cited by 6 publications

References 32 publications

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals

Synchronized Detection of Evoked Potentials to Drive a High Information Transfer Rate Hybrid Brain-Computer Interface Application

Use of the Stockwell Transform in the Detection of P300 Evoked Potentials with Low-Cost Brain Sensors

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