Brain–computer interface (BCI) connects the outside world, in real time and in a natural way, like biological communication system. It facilitates the communication link from the brain to the external world by converting brain thoughts in to control commands to control the external devices, such as wheelchair, keyboard mouse, and other home appliances. Measuring the electrical brain activity by placing electrodes over scalp is called electroencephalogram (EEG). By combining these two techniques, we are able to create EEG-based BCI. In this paper, we use band power and radial basis function to analyze the signal for four mentally composed tasks to design four states BCI for a neurodegenerative person using EEG. Online study was conducted to analyze the performance of the wheelchair for a neurodegenerative person. The result shows that an overall average classification accuracy of 92.50% and individual tasks with an average classification of 95%, 87.50%, 92.50%, and 95.00% were achieved for the four tasks. The result proves that control commands generated from the EEG signal have the bcapacity to control the intelligent systems.

Currently majority of persons were immobilized and need aid from care takers due to disability. To reduce and overcome such problem there was a need for developing Human Computer Interface (HCI) with the help of bio signals. In this paper we propose a two channel Elecctrooculograpy (EOG) based HCI to encourage the contact ability as well as value of life for paralyzed persons who cannot speak or shift their extremity by using twenty subjects with the help of ADT26 Bio amplifier. EOG signals were collected for eleven tasks from both vertical and horizontal eye movement by using gold platted electrodes. The extracted EOG signals were processed with convolution and plancherel theorem to obtain the features. Layered Recurrent Neural Network (LRNN) was implemented to analyze the extracted features and then converted in to sequence of commands to control the HCI. A Graphical user Interface was developed using Matlab to help a user to convey their thoughts. Our study shows an average classification accuracy of 90.72% for Convolution features and 91.28% for Plancherel features. Offline Single trail analysis was also performed to analyze the recognition accuracy of the proposed HCI system. The offline analysis displayed that Plancherel features using LRNN was high compared to Convolution features using LRNN. From this study we found that LRNN architecture using Plancherel features was more suitable for developing EOG based HCI. Single trail analysis was conducted to identify the recognizing accuracy in offline. The offline results indicated that in comparison with other EOG-based HCI systems, our system was user friendly and needs minimum training to operate.

Glaucoma is a disease where the optic nerve of the eyes is smashed up due to the building up of pressure inside the vision point. This has no symptoms at the initial stages, and hence, patients with this disease cannot identify them at the beginning stage. It is explained as if the pressure in the eye increases, then it will hurt the optic nerve which sends images to the brain. This will lead to permanent vision loss or total blindness. The existing method used for the detection of glaucoma includes k-nearest neighbour and support vector machine algorithms. The k-nearest neighbour algorithm and support vector machine algorithm are the machine learning methods for both categorization and degeneration problems. The drawback in using these algorithms is that we can get accuracy level only up to 80. The proposed methods in this study focus on the convolution neural network for the recognition of glaucoma. In this study, 2 architectures of VGG, Inception method, AlexNet, GoogLeNet, and ResNet architectures which provide accuracy levels up to 100 are presented.

Epilepsy is a neurological disease condition in which one experience seizures. These can be recorded using Electroencephalogram which is by nature long term recordings. A lot of work has been done towards automatic detection of these seizures in the literature with both short term and long term recordings. In this work, we have used the standard database of University of Bonn for a four different two class classification problem as addressed by various others in the literature. We have used novel single feature namely Tsallis entropy along with five different classifiers. Comparing with other literatures, we find that our method has the least computation time as low as 0.9 ms. We achieved a highest accuracy of 92.67–100% with Decision tree classifier for the four types of two class classification problem considered. Our method being very simple and also has fastest computation time in comparison with other features in the literature and thus can form as a software tool that can be installed easily and also opens future opportunities towards real time detection and prediction of epileptic seizures.

Breast cancer is an important factor affecting human health. This issue has various diagnosis process which were evolved such as mammography, fine needle aspirate, and surgical biopsy. These techniques use pathological breast cancer images for diagnosis. Breast cancer surgery allows the forensic doctor to histologist to access the microscopic level of breast tissues. The conventional method uses an optimized radial basis neural network using a cuckoo search algorithm. Existing radial basis neural network techniques utilized feature extraction and reduction parts separately. It is proposed that it overcomes the CNN approach for all the feature extraction and classification process to reduce time complexity. In this proposed method, a convolutional neural network is proposed based on an artificial fish school algorithm. The breast cancer image dataset is taken from cancer imaging archives. In the preprocessing step of classification, the breast cancer image is filtered with the support of a wiener filter for classification. The convolutional neural network has set the intense data of an image and is used to remove the features. After executing the extraction procedure, the reduction process is performed to speed up the train and test data processing. Here, the artificial fish school optimization algorithm is utilized to give the direct training data to the deep convolutional neural network. The extraction, reduction, and classification of features are utilized in the single deep convolutional neural network process. In this process, the optimization technique helps to decrease the error rate and increases the performance efficiency by finding the number of epochs and training images to the Deep CNN. In this system, the normal, benign, and malignant tissues are predicted. By comparing the existing RBF technique with the cuckoo search algorithm, the presented model attains the outcome in the way of sensitivity, accuracy, specificity, F1 score, and recall.