Zaghloul Saad Zaghloul

Bio: Zaghloul Saad Zaghloul is an academic researcher from University of Cincinnati. The author has contributed to research in topics: Spike sorting & Epilepsy. The author has an hindex of 3, co-authored 16 publications receiving 42 citations. Previous affiliations of Zaghloul Saad Zaghloul include Information Technology University & University of Louisiana at Lafayette.

Brain Computer Interface: EEG Signal Preprocessing Issues and Solutions

Abstract: Brain Computer Interface (BCI) is often directed at mapping, assisting, or repairing human cognitive or sensory-motor functions. Electroencephalogram (EEG) is a non-invasive method of acquisition brain electrical activities. Noises are impure the EEG recorded signal due to the physiologic and extra-physiologic artifacts. There are several techniques are intended to manipulate the EEG recorded signal during the BCI preprocessing stage of to achieve preferable results at the learning stage. This paper aims to present an overview on BCI different EEG brain signal recording artifacts and the methodologies to remove these artifacts from the signal focusing on different novel trends at BCI research areas.

Intrusion Detection System in Smart Home Network Using Bidirectional LSTM and Convolutional Neural Networks Hybrid Model

Abstract: Internet of Things (IoT) allowed smart homes to improve the quality and the comfort of our daily lives. However, these conveniences introduced several security concerns that increase rapidly. IoT devices, smart home hubs, and gateway raise various security risks. The smart home gateways act as a centralized point of communication between the IoT devices, which can create a backdoor into network data for hackers. One of the common and effective ways to detect such attacks is intrusion detection in the network traffic. In this paper, we proposed an intrusion detection system (IDS) to detect anomalies in a smart home network using a bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) hybrid model. The BiLSTM recurrent behavior provides the intrusion detection model to preserve the learned information through time, and the CNN extracts perfectly the data features. The proposed model can be applied to any smart home network gateway.

Adaptive neural matching online spike sorting VLSI chip design for wireless BCI implants

Abstract: Controlling the surrounding world by just the power of our thoughts has always seemed to be just a fictional dream. With recent advancements in technology and research, this dream has become a reality for some through the use of a Brain Computer/Machine Interface (BCI/BMI). One of the most important goals of BCI is to enable handicap people to control artificial limbs. Some research proposed wireless implants that do not require chronic wound in the skull. However, the communications consume a high bandwidth and power that exceeds the allowed limits, 8–10mW. This study proposes and implements a modified version of real-time spike sorting for wireless BCI [4] that simplifies and uses less computation via an adaptive neural-structure; which makes it simpler, faster and power and area efficient. The system was implemented, and simulated using Modalism and Cadence, with ideal case and worst case accuracy of 100% and 91.7%, respectively. Also, the chip layout of 0.704mm2, with power consumption of 4.7mW and was synthesized on 45nm technology using Synopsys.

Implementable Spike Sorting techniques for VLSI wireless BCI/BMI implants: A survey

Abstract: Brain Computer/Machine Interface (BCI/BMI) has a great potential in solving many handicapped people's problems (e.g. restoring missing limb functionality) via neural controlled implants. However, the main problems with BCI implants are: the open wound problems, the implant size and the needed power. This limits the usability of BCI implants for patients. Therefore, a wireless VLSI approach can provide a feasible solution for medically safe and usable implants. Spike Sorting play a major role in power consumption and area within the BCI signal processing stages. Thus, this study provides a survey and an analysis of the Signal Processing and Spike Sorting methods being used or proposed for VLSL wireless BCI Implants, along with a suggested design and a set of recommendations as a step closer to real-life BCI implants.

Early Prediction of Epilepsy Seizures System based on Artificial Immune BCI System

Abstract: Controlling the surrounding world and predicting future events has always seemed like a dream, but that could become a reality using a Brain Computer/Machine Interface (BCI/BMI). Epilepsy is a group of neurological diseases characterized by epileptic seizures. It affects millions of people worldwide, with 80% of cases occurring in developing countries. This can result in accidents and sudden, unexpected death. Seizures can happen undetectably in newborns, comatose, or motor impaired patients, especially due to the fact that many medical personnel are not qualified for EEG signal analysis. Therefore, a portable automated detection and monitoring solution is in high demand. Thus, in this study a system of a wireless wearable adaptive for early prediction of epilepsy seizures is proposed, works via minimally invasive wireless technology paired with an external control device (e.g., a doctors’ smartphone), with a higher than standard accuracy (71%) and prediction time (14.56 sec). This novel architecture has not only opened new opportunities for daily usable BCI implementations, but they can also save a life by helping to prevent a seizure’s fatal consequences.

Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals

Abstract: In recent years, stress analysis by using electro-encephalography (EEG) signals incorporating machine learning techniques has emerged as an important area of research. EEG signals are one of the most important means of indirectly measuring the state of the brain. The existing stress algorithms lack efficient feature selection techniques to improve the performance of a subsequent classifier. In this paper, genetic algorithm (GA)-based feature selection and k-nearest neighbor (k-NN) classifier are used to identify stress in human beings by analyzing electro-encephalography (EEG) signals. GA is incorporated in the stress analysis pipeline to effectively select subset of features that are suitable to enhance the performance of the k-NN classifier. The performance of the proposed method is evaluated using the Database for Emotion Analysis using Physiological Signals (DEAP), which is a public EEG dataset. A feature set is extracted in 32 EEG channels, which consists of statistical features, Hjorth parameters, band power, and frontal alpha asymmetry. The selected features through GA are used as input to the k-NN classifier to distinguish whether each EEG datapoint represents a stress state. To further consolidate, the effectiveness of the proposed method is compared with that of a state-of-the-art principle component analysis (PCA) method. Experimental results show that the proposed GA-based method outperforms PCA, with GA demonstrating 71.76% classification accuracy compared with 65.3% for PCA. Thus, it can be concluded that the proposed method can be effectively used for stress analysis with high classification accuracy.

Brain Computer Interface: EEG Signal Preprocessing Issues and Solutions

Abstract: Brain Computer Interface (BCI) is often directed at mapping, assisting, or repairing human cognitive or sensory-motor functions. Electroencephalogram (EEG) is a non-invasive method of acquisition brain electrical activities. Noises are impure the EEG recorded signal due to the physiologic and extra-physiologic artifacts. There are several techniques are intended to manipulate the EEG recorded signal during the BCI preprocessing stage of to achieve preferable results at the learning stage. This paper aims to present an overview on BCI different EEG brain signal recording artifacts and the methodologies to remove these artifacts from the signal focusing on different novel trends at BCI research areas.