Browsing by Author "KAMHI, SOUHA"

Now showing 1 - 4 of 4
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    Analysis And Classification Of Motor Imagery Using Deep Neural Network
    (institution of Applied Materials and Technology Society with the cooperation of Faculty of Engineering, Universitas Riau, Pekanbaru, Indonesia, 2021-05-25) Ahmad, Isah Salim; Zhang, Shuai; Saminu, Sani; Isselmou; Musa, Jamilu Maaruf; Javaid, Imran; KAMHI, SOUHA; KULSUM, UMMAY
    Motor imagery based on brain-computer interface (BCI) has aĨracted important research aĨention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalogram (EEG) signals to study brain activity with leě and right-hand movement. Deep learning (DL) has been employed for motor imagery (MI). In this article, a deep neural network (DNN) is proposed for classiėcation of leě and right movement of EEG signal using Common Spatial PaĨern (CSP) as feature extraction with standard gradient descent (GD) with momentum and adaptive learning rate LR. (GDMLR), the performance is compared using a confusion matrix, the average classiėcation accuracy is 87%, which is improved as compared with state-of-the-art methods that used different datasets.
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    Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto‐Encoder Model
    (MDPI, 2021-08-31) Isselmou, Abd El Kader; Guizhi, Xu; Zhang, Shuai; Saminu, Sani; Javaid, Imran; Ahmad, Isah Salim; KAMHI, SOUHA
    The process of diagnosing brain tumors is very complicated for many reasons, including the brain’s synaptic structure, size, and shape. Machine learning techniques are employed to help doctors to detect brain tumor and support their decisions. In recent years, deep learning techniques have made a great achievement in medical image analysis. This paper proposed a deep wavelet autoencoder model named “DWAE model”, employed to divide input data slice as a tumor (abnor‐ mal) or no tumor (normal). This article used a high pass filter to show the heterogeneity of the MRI images and their integration with the input images. A high median filter was utilized to merge slices. We improved the output slices’ quality through highlight edges and smoothened input MR brain images. Then, we applied the seed growing method based on 4‐connected since the thresholding cluster equal pixels with input MR data. The segmented MR image slices provide two two‐layer using the proposed deep wavelet auto‐encoder model. We then used 200 hidden units in the first layer and 400 hidden units in the second layer. The softmax layer testing and training are performed for the identification of the MR image normal and abnormal. The contribution of the deep wavelet auto‐encoder model is in the analysis of pixel pattern of MR brain image and the ability to detect and classify the tumor with high accuracy, short time, and low loss validation. To train and test the overall performance of the proposed model, we utilized 2500 MR brain images from BRATS2012, BRATS2013, BRATS2014, BRATS2015, 2015 challenge, and ISLES, which consists of normal and ab‐ normal images. The experiments results show that the proposed model achieved an accuracy of 99.3%, loss validation of 0.1, low FPR and FNR values. This result demonstrates that the proposed DWAE model can facilitate the automatic detection of brain tumors.
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    Convolutional Neural Networks Model for Emotion Recognition Using EEG Signal
    (North Atlantic University Union, 2021-04-29) Ahmad, Isah Salim; Zhang, Shuai; WANG, LINGYUE; Saminu, Sani; Isselmou, Abd El Kader; CAI, ZILIANG; Javaid, Imran; KAMHI, SOUHA; KULSUM, UMMAY
    A Brain-computer interface (BCI) using an electroencephalogram (EEG) signal has a great attraction in emotion recognition studies due to its resistance to humans’ deceptive actions. This is the most significant advantage of brain signals over speech or visual signals in the emotion recognition context. A major challenge in EEG-based emotion recognition is that a lot of effort is required for manually feature extractor, EEG recordings show varying distributions for different people and the same person at different time instances. The Poor generalization ability of the network model as well as low robustness of the recognition system. Improving algorithms and machine learning technology helps researchers to recognize emotion easily. In recent years, deep learning (DL) techniques, specifically convolutional neural networks (CNNs) have made excellent progress in many applications. This study aims to reduce the manual effort on features extraction and improve the EEG signal single model’s emotion recognition using convolutional neural network (CNN) architecture with residue block. The dataset is shuffle, divided into training and testing, and then fed to the model. DEAP dataset has class 1, class 2, class 3, and class 4 for both valence and arousal with an accuracy of 90.69%, 91.21%, 89.66%, 93.64% respectively, with a mean accuracy of 91.3%. The negative emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive emotion with 93.25% respectively, with a mean accuracy of 94.13% on the SEED dataset. The experimental results indicated that CNN Based on residual networks can achieve an excellent result with high recognition accuracy, which is superior to most recent approaches.
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    Deep Learning Based on CNN for Emotion Recognition Using EEG Signal
    (WSEAS, 2021-04-14) Ahmad, Isah Salim; Zhang, Shuai; Saminu, Sani; WANG, LINGYUE; Isselmou, Abd El Kader; CAI, ZILIANG; Javaid, Imran; KAMHI, SOUHA; KULSUM, UMMAY
    Emotion recognition based on brain-computer interface (BCI) has attracted important research attention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalograms (EEG) signals to study emotion because of its easy and convenient. Deep learning has been employed for the emotion recognition system. It recognizes emotion into single or multi-models, with visual or music stimuli shown on a screen. In this article, the convolutional neural network (CNN) model is introduced to simultaneously learn the feature and recognize the emotion of positive, neutral, and negative states of pure EEG signals single model based on the SJTU emotion EEG dataset (SEED) with ResNet50 and Adam optimizer. The dataset is shuffle, divided into training and testing, and then fed to the CNN model. The negative emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive emotion with 93.25% respectively. With average accuracy of 94.13%. The results showed excellent classification ability of the model and can improve emotion recognition.