Single channel wireless EEG device for real-time fatigue level detection

© 2015 IEEE. Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many research had investigated that using EEG signals can effectively detect driver's drowsiness level. However, real-time monitoring system is required to apply these fatigue level detection techniques in the practical application, especially in the real-road driving. Therefore, it required less channels, portable and wireless, real-time monitoring and processing techniques for developing the real-time monitoring system. In this study, we develop a single channel wireless EEG device which can real-time detect driver's fatigue level on the mobile device such as smart phone or tablet. The developed device is investigated to obtain a better and precise understanding of brain activities of mental fatigue under driving, which is of great benefit for devolvement of detection of driving fatigue system. This system consists of a Bluetooth-enabled one channel EEG, a regression model, and smartphone, which was a platform recording and transforming the raw EEG data to useful driving status. In the experiment, this was a sustained-attention driving task to implement in a virtual-reality (VR) driving simulator. To training model and develop the system, we were performed for 15 subjects to study Electroencephalography (EEG) brain dynamics by using a mobile and wireless EEG device. Based on the outstanding training results, the leave-one-subject-out cross validation test obtained 90% fatigue detection accuracy. These results indicate that the combination of a smartphone and wireless EEG device constitutes an effective and easy wearable solution for detecting and preventing driver fatigue in real driving environments

A novel 16-channel wireless system for electroencephalography measurements with dry spring-loaded sensors

Understanding brain function using electroencephalography (EEG) is an important issue for cerebral nervous system diseases, especially for epilepsy and Alzheimer's disease. Many EEG measurement systems are used reliably to study these diseases, but their bulky size and the use of wet sensors make them uncomfortable and inconvenient for users. To overcome the limitations of conventional EEG measurement systems, a wireless and wearable multichannel EEG measurement system is proposed in this paper. This system includes a wireless data acquisition device, dry spring-loaded sensors, and a sizeadjustable soft cap. We compared the performance of the proposed system using dry versus conventional wet sensors. A significant positive correlation between readings from wet and dry sensors was achieved, thus demonstrating the performance of the system. Moreover, four different features of EEG signals (i.e., normal, eye-blinking, closed-eyes, and teeth-clenching signals) were measured by 16 dry sensors to ensure that they could be detected in real-life cognitive neuroscience applications. Thus, we have shown that it is possible to reliably measure EEG signals using the proposed system. This paper presents novel insights into the field of cognitive neuroscience, showing the possibility of studying brain function under real-life conditions. © 2014 IEEE

Development of Application Specific Clustering Protocols for Wireless Sensor Networks

Applications in wireless sensor networks (WSNs) span over various areas like weather forecasting to measuring soil parameters in agriculture, and from battle_eld to health monitoring. Constrained battery power of sensor nodes make the network design a challenging task. Amongst several research areas in WSN, designing energy e_cient protocols is a prominent area. Clustering is a proven solution to enhance the network lifetime by utilizing the availablebattery power e_ciently. In this thesis, a hypothetical overview has been done to study the strengths and weaknesses of existing clustering algorithms that inspired the design of distributed and energy e_cient clustering in WSN. Distributed Dynamic Clustering Protocol (DDCP) has been proposed to allow all the nodes to take part in the cluster formation scheme and data transmission process. This protocol consists of a cluster-head selection algorithm, a cluster formation scheme and a routing algorithm for the data transmission between cluster-heads and the base station. All the sensor nodes present in the network takes part in the cluster-head selection process. Staggered Clustering Protocol (SCP) has been proposed to develop a new energy e_cient clustering protocol for WSN. This algorithm is aiming at choosing cluster-heads that ensure both the intra-cluster data transmission and inter-cluster data transmission are energy-e_cient. The cluster formation scheme is accomplished by exchanging messages between non-cluster-head nodes and the cluster-head to ensure a balanced energy loadamong cluster-heads. An energy e_cient clustering algorithm for wireless sensor networks using particle swarm optimization (EEC-PSO) has been proposed to ensure energy e_ciency by creating optimized number of clusters. It also improves the link quality among the cluster-heads with the cluster member nodes. Finding a set of suitable cluster-heads from N sensor nodes is considered as non-deterministic polynomial (NP)-hard optimization problem. The application of WSN in brain computer interface (BCI) has been proposed to detect the drowsiness of a driver on wheels. The sensors placed in a braincap worn by the driver are divided into small clusters. Then the sensed data, known as EEG signal, are transferred towards the base station through the cluster-heads. The base station may be placed at a nearby location of the driver. The received data is processed to take a decision when to trigger the warning tone

Классификация моторных образов с помощью глубокого обучения и графического представления электроэнцефалограмм

Целью данной работы является разработка модели классификации моторных образов на основе инструментов глубокого обучения и преобразования Gramian Angular Field. В выпускной квалификационной работе рассмотрены современные подходы в проектировании систем BCI, извлечении признаков и классификации электроэнцефалограмм. Подготовлены обучающие данные и проведены эксперименты на различных архитектурах глубоких сетей для поиска оптимальных параметров входных данных, а также протестирован метод классификации, учитывающий соседние временные окна сигналов. Была разработана и оптимизирована модель нейронной сети, а также приведены результаты её обучения.The aim of this work is to develop a model of motor image classification based on deep learning tools and Gramian Angular Field transformation. In this paper the modern approaches in BCI system design, feature extraction and classification of electroencephalograms are considered. Training data were prepared and experiments were conducted on different deep network architectures to find the optimal input data parameters, and a classification method was tested that takes into account the neighboring time windows of signals. A neural network model was developed and optimized, and the results of its training were presented

Foundations and applications of human-machine-interaction

Der vorliegende Tagungsband zur 10. Berliner Werkstatt Mensch-Maschine-Systeme gibt einen Einblick in die aktuelle Forschung im Bereich der Mensch-Maschine- Interaktion. Einen besonderen Fokus stellt das Wechselspiel von Grundlagenforschung und anwendungsbezogener Forschung dar, was sich im breiten Themenspektrum widerspiegelt, welches von theoretischen und methodischen Betrachtungen bis hin zu anwendungsnahen Fragestellungen reicht. Dabei finden Inhalte aus allen Phasen des Forschungsprozesses Beachtung, sodass auch im Rahmen der 10. Berliner Werkstatt MMS wieder sowohl neue Untersuchungskonzepte als auch abschließende Befunde diskutiert werden. Zentrale Themengebiete sind u. a. Fahrer-Fahrzeug-Interaktion, Assistenzsysteme, User Experience, Usability, Ubiquitous Computing, Mixed & Virtual Reality, Robotics & Automation, Wahrnehmungsspezifika sowie Psychophysiologie und Beanspruchung in der Mensch-Maschine-Interaktion.The proceedings of the 10th Berlin Workshop Human-Machine-Systems provide an insight into the current research in the field of human-machine-interaction. The main focus lies on the interplay between basic and applied research, which is reflected in the wide range of subjects: from theoretical and methodological issues to application oriented considerations. Again all stages of the research process are represented in the contributions of the 10th Berlin Workshop HMS. This means new research concepts as well as final results are subject of this volume. Central topics include driver-vehicleinteraction, assistance systems, user experience, usability, ubiquitous computing, mixed and virtual reality, robotics & automation, perception specifics, as well as psychophysiology and workload in human-machine-interaction

CMOS Hyperbolic Sine ELIN filters for low/audio frequency biomedical applications

Hyperbolic-Sine (Sinh) filters form a subclass of Externally-Linear-Internally-Non- Linear (ELIN) systems. They can handle large-signals in a low power environment under half the capacitor area required by the more popular ELIN Log-domain filters. Their inherent class-AB nature stems from the odd property of the sinh function at the heart of their companding operation. Despite this early realisation, the Sinh filtering paradigm has not attracted the interest it deserves to date probably due to its mathematical and circuit-level complexity. This Thesis presents an overview of the CMOS weak inversion Sinh filtering paradigm and explains how biomedical systems of low- to audio-frequency range could benefit from it. Its dual scope is to: consolidate the theory behind the synthesis and design of high order Sinh continuous–time filters and more importantly to confirm their micro-power consumption and 100+ dB of DR through measured results presented for the first time. Novel high order Sinh topologies are designed by means of a systematic mathematical framework introduced. They employ a recently proposed CMOS Sinh integrator comprising only p-type devices in its translinear loops. The performance of the high order topologies is evaluated both solely and in comparison with their Log domain counterparts. A 5th order Sinh Chebyshev low pass filter is compared head-to-head with a corresponding and also novel Log domain class-AB topology, confirming that Sinh filters constitute a solution of equally high DR (100+ dB) with half the capacitor area at the expense of higher complexity and power consumption. The theoretical findings are validated by means of measured results from an 8th order notch filter for 50/60Hz noise fabricated in a 0.35μm CMOS technology. Measured results confirm a DR of 102dB, a moderate SNR of ~60dB and 74μW power consumption from 2V power supply