A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs

Attallah, Omneya; Abougharbia, Jaidaa; Tamazin, Mohamed; Nasser, A. A. A.

doi:10.3390/brainsci10110864

Cited by 43 publications

(35 citation statements)

References 63 publications

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“…Table V shows the results obtained on the dataset IVa employing a Nested KFold cross-validation procedure with the whole dataset, similar to previous studies such as [70] and [71], but considering all EEG channels. These studies applied a KFold cross-validation using selected channels, obtaining promising results.…”

Section: A Experimentssupporting

confidence: 60%

“…For instance, Miao et al [70] converted the raw EEG into image representation by computing its energy for different frequency bands, and further used a multilayer CNN, obtaining mean ACC of 90%. Attallah et al [71] proposed a hybrid system to calculate a feature set through different combinations of channels, obtaining the best performance (mean ACC of 93.46%) for 18 channels by using SVM.…”

Section: Sakhavi and Guanmentioning

confidence: 99%

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Shallow Convolutional Network Excel for Classifying Motor Imagery EEG in BCI Applications

Hermosilla

Codorniú

Baracaldo³

et al. 2021

IEEE Access

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Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for rehabilitation have demonstrated the important role of detecting the Event-Related Desynchronization (ERD) to recognize the user's motor intention. Nowadays, the development of MI-based BCI approaches without or very few calibration stages session-by-session for different days or weeks is still an open and emergent scope. In this work, a new scheme is proposed by applying Convolutional Neural Networks (CNN) for MI classification, using an end-to-end Shallow architecture that contains two convolutional layers for temporal and spatial feature extraction. We hypothesize that a BCI designed for capturing event-related desynchronization/synchronization (ERD/ERS) at the CNN input, with an adequate network design, may enhance the MI classification with less calibration stage. The proposed system using the same architecture was tested on three public datasets through multiple experiments, including both subject-specific and nonsubject-specific training. Comparable and also superior results with respect to the state-of-the-art were obtained. On subjects whose EEG data were never used in the training process, our scheme also achieved promising results with respect to existing non-subject-specific BCIs, which is other progress to facilitate the clinical application.

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Section: A Experimentssupporting

confidence: 60%

Section: Sakhavi and Guanmentioning

confidence: 99%

Shallow Convolutional Network Excel for Classifying Motor Imagery EEG in BCI Applications

Hermosilla

Codorniú

Baracaldo³

et al. 2021

IEEE Access

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show abstract

“…This extension can provide multi-level signal/image conversion from the time domain to the time-frequency domain. WPD process is made by passing an image/signal x(n) to a high and low pass filter denoted as g(n) and h(n) respectively ( Attallah et al, 2020 ) and then down-sampling by 2 is done. The filtering process is equivalent to convolving low pass and high pass filters with a signal/image.…”

Section: Methodsmentioning

confidence: 99%

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Attallah

Anwar

Ghanem

et al. 2021

PeerJ Computer Science

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Breast cancer (BC) is one of the most common types of cancer that affects females worldwide. It may lead to irreversible complications and even death due to late diagnosis and treatment. The pathological analysis is considered the gold standard for BC detection, but it is a challenging task. Automatic diagnosis of BC could reduce death rates, by creating a computer aided diagnosis (CADx) system capable of accurately identifying BC at an early stage and decreasing the time consumed by pathologists during examinations. This paper proposes a novel CADx system named Histo-CADx for the automatic diagnosis of BC. Most related studies were based on individual deep learning methods. Also, studies did not examine the influence of fusing features from multiple CNNs and handcrafted features. In addition, related studies did not investigate the best combination of fused features that influence the performance of the CADx. Therefore, Histo-CADx is based on two stages of fusion. The first fusion stage involves the investigation of the impact of fusing several deep learning (DL) techniques with handcrafted feature extraction methods using the auto-encoder DL method. This stage also examines and searches for a suitable set of fused features that could improve the performance of Histo-CADx. The second fusion stage constructs a multiple classifier system (MCS) for fusing outputs from three classifiers, to further improve the accuracy of the proposed Histo-CADx. The performance of Histo-CADx is evaluated using two public datasets; specifically, the BreakHis and the ICIAR 2018 datasets. The results from the analysis of both datasets verified that the two fusion stages of Histo-CADx successfully improved the accuracy of the CADx compared to CADx constructed with individual features. Furthermore, using the auto-encoder for the fusion process has reduced the computation cost of the system. Moreover, the results after the two fusion stages confirmed that Histo-CADx is reliable and has the capacity of classifying BC more accurately compared to other latest studies. Consequently, it can be used by pathologists to help them in the accurate diagnosis of BC. In addition, it can decrease the time and effort needed by medical experts during the examination.

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“…To further reduce the vast dimension of the fused feature sets chosen in the previous phase, feature selection is essential (Attallah et al, 2017b) because the massive size of the features raises the complexity of the classification phase and might decrease its performance (Li and Liu, 2017;Hatamimajoumerd et al, 2020). Feature selection is frequently utilized in medical frameworks to reduce the dimension of the feature set and delete unnecessary and irrelevant variables (Chandrashekar and Sahin, 2014;Attallah et al, 2017aAttallah et al, , 2020aCai et al, 2018). In this phase, two feature selection approaches, including Relief-F and Information gain (IG) feature selection methods are used to select a reduced set of features.…”

Section: Feature Selection Phasementioning

confidence: 99%

CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes

Attallah

2021

Front. Neuroinform.

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Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is believed to be the foremost common pediatric brain tumor causing death. Early and accurate classification of childhood MB and its classes are of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. The current gold standard for diagnosing MB is the histopathology of biopsy samples. However, manual analysis of such images is complicated, costly, time-consuming, and highly dependent on the expertise and skills of pathologists, which might cause inaccurate results. This study aims to introduce a reliable computer-assisted pipeline called CoMB-Deep to automatically classify MB and its classes with high accuracy from histopathological images. This key challenge of the study is the lack of childhood MB datasets, especially its four categories (defined by the WHO) and the inadequate related studies. All relevant works were based on either deep learning (DL) or textural analysis feature extractions. Also, such studies employed distinct features to accomplish the classification procedure. Besides, most of them only extracted spatial features. Nevertheless, CoMB-Deep blends the advantages of textural analysis feature extraction techniques and DL approaches. The CoMB-Deep consists of a composite of DL techniques. Initially, it extracts deep spatial features from 10 convolutional neural networks (CNNs). It then performs a feature fusion step using discrete wavelet transform (DWT), a texture analysis method capable of reducing the dimension of fused features. Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. A bi-directional long-short term memory (Bi-LSTM) network; a DL-based approach that is utilized for the classification phase. CoMB-Deep maintains two classification categories: binary category for distinguishing between the abnormal and normal cases and multi-class category to identify the subclasses of MB. The results of the CoMB-Deep for both classification categories prove that it is reliable. The results also indicate that the feature sets selected using both search strategies have enhanced the performance of Bi-LSTM compared to individual spatial deep features. CoMB-Deep is compared to related studies to verify its competitiveness, and this comparison confirmed its robustness and outperformance. Hence, CoMB-Deep can help pathologists perform accurate diagnoses, reduce misdiagnosis risks that could occur with manual diagnosis, accelerate the classification procedure, and decrease diagnosis costs.

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A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs

Cited by 43 publications

References 63 publications

Shallow Convolutional Network Excel for Classifying Motor Imagery EEG in BCI Applications

Shallow Convolutional Network Excel for Classifying Motor Imagery EEG in BCI Applications

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes

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