Brain computer interface: control signals review

People with severe neurological impairments face many challenges in sensorimotor functions and communication with the environment; therefore they have increased demand for advanced, adaptive and personalized rehabilitation. During the last several decades, numerous studies have developed brain-computer interfaces (BCIs) with the goals ranging from providing means of communication to functional rehabilitation. Here we review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. We focus on the approaches intended to help severely paralyzed and locked-in patients regain communication using three different BCI modalities: slow cortical potentials, sensorimotor rhythms and P300 potentials, as operational mechanisms. We also review BCI systems for restoration of motor function in patients with spinal cord injury and chronic stroke. We discuss the advantages and limitations of these approaches and the challenges that need to be addressed in the future.

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EEG-Based Brain-Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21 st Century.

Load balancing in cloud computing: A big picture

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

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Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Histograms of local appearance descriptors are a popular representation for visual recognition. They are highly discriminant and have good resistance to local occlusions and to geometric and photometric variations, but they are not able to exploit spatial co-occurrence statistics at scales larger than their local input patches. We present a new multilevel visual representation, 'hyperfeatures', that is designed to remedy this. The starting point is the familiar notion that to detect object parts, in practice it often suffices to detect co-occurrences of more local object fragments - a process that can be formalized as comparison (e.g. vector quantization) of image patches against a codebook of known fragments, followed by local aggregation of the resulting codebook membership vectors to detect co-occurrences. This process converts local collections of image descriptor vectors into somewhat less local histogram vectors - higher-level but spatially coarser descriptors. We observe that as the output is again a local descriptor vector, the process can be iterated, and that doing so captures and codes ever larger assemblies of object parts and increasingly abstract or 'semantic' image properties. We formulate the hyperfeatures model and study its performance under several different image coding methods including clustering based Vector Quantization, Gaussian Mixtures, and combinations of these with Latent Dirichlet Allocation. We find that the resulting high-level features provide improved performance in several object image and texture image classification tasks.

Hyperfeatures : Multilevel Local Coding for Visual Recognition

Brain computer interfacing: Applications and challenges

Human activity recognition is an important area of machine learning research as it has many utilization in different areas such as sports training, security, entertainment, ambient-assisted living, and health monitoring and management Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human Therefore, the general architecture of HAR system is presented in this paper, along with the description of its main components The state of the art in human activity recognition based on accelerometer is surveyed According to this survey, Most of the researches recently used deep learning for recognizing HAR, but they focused on CNN even though there are other deep learning types achieved a satisfied accuracy The paper displays a two-level taxonomy in accordance with machine learning approach (either traditional or deep learning) and the processing mode (either online or offline) Forty eight studies are compared in terms of recognition accuracy, classifier, activities types, and used devices Finally, the paper concludes different challenges and issues online versus offline also using deep learning versus traditional machine learning for human activity recognition based on accelerometer sensors

/pdf/survey-on-human-activity-recognition-based-on-acceleration-56hbzt9ahc.pdf

Survey on Human Activity Recognition based on Acceleration Data

Road﻿traffic﻿accidents﻿are﻿caused﻿1.25﻿million﻿deaths﻿per﻿year﻿worldwide.﻿To﻿improve﻿road﻿safety﻿ and﻿reducing﻿road﻿accidents,﻿a﻿recognition﻿method﻿for﻿driving﻿events﻿is﻿introduced﻿in﻿this﻿paper.﻿The﻿ proposed﻿method﻿detected﻿and﻿classified﻿both﻿driving﻿behaviors﻿and﻿road﻿anomalies﻿patterns﻿based﻿on﻿ smartphone﻿sensors﻿(accelerometer﻿and﻿gyroscope).﻿k-Nearest﻿Neighbor﻿and﻿Dynamic﻿Time﻿Warping﻿ algorithms﻿were﻿utilized﻿for﻿method﻿evaluation.﻿Experiments﻿were﻿conducted﻿to﻿evaluate﻿k-nearest﻿ neighbor﻿and﻿dynamic﻿time﻿warping﻿algorithms﻿accuracy﻿for﻿road﻿anomalies﻿and﻿driving﻿behaviors﻿ detection,﻿moreover,﻿driving﻿behaviors﻿classification.﻿Evaluation﻿results﻿showed﻿that﻿k-nearest﻿neighbor﻿ algorithm﻿detected﻿road﻿anomalies﻿and﻿driving﻿behaviors﻿with﻿total﻿accuracy﻿98.67%.﻿Dynamic﻿time﻿ warping﻿algorithm﻿classified﻿(normal﻿and﻿abnormal)﻿driving﻿behaviors﻿with﻿total﻿accuracy﻿96.75%. KeywoRDS Anomaly Detection, Behavior Classification, Driving Behavior, Road Anomalies, Smartphone Sensors

Recognizing Driving Behavior and Road Anomaly Using Smartphone Sensors

The daily activities recognition is one of the most important areas that attract the attention of researchers. Automatic classification of activities of daily living (ADL) can be used to promote healthier lifestyle, though it can be challenging when it comes to intellectual disability personals, the elderly, or children. Thus developing a technique to recognize activities with high quality is critical for such applications. In this work, seven algorithms are developed and evaluated for classification of everyday activities like climbing the stairs, drinking water, getting up from bed, pouring water, sitting down on a chair, standing up from a chair, and walking. Algorithms of concern are K-nearest Neighbor, Artificial Neural Network, and Naive Bayes, Dynamic Time Warping, $1 recognizer, Support Vector Machine, and a novel classifier (D$1). We explore different algorithm activities with regard to recognizing everyday activities. We also present a technique based on $1 and DTW to enhance the recognition accuracy of ADL. Our result show that we can achieve up to 83 % accuracy for seven different activities.

An Experimental Comparison Between Seven Classification Algorithms for Activity Recognition

Features detection and description among multiple images are widely used in many applications, e.g., feature matching, object categorization, 3D construction, image retrieval and object recognition. This paper evaluates combination performance of different feature detectors and descriptors. It will compare performance of detectors and descriptors combination on images under rotate, scale constraints and distortion such as illumination on different scene (bedroom, industrial and CALsuburb). An experimental result shows MinEigen detector has best result in number of detected key-points when handle rotate, scale and illumination and not affected with scene. SURF without external detector is the best when handle rotate and scale constraint in different levels and scene. FAST/SURF and Harris/FREAK are best combined against illumination distortion in different levels. This review introduces a brief introduction for providing a new research in feature detection field to find appropriate method according to their condition.

http://ijcsi.org/papers/IJCSI-12-5-87-94.pdf

Mostafa-Sami M. Mostafa

Papers

Brain computer interfacing: Applications and challenges

Survey on Human Activity Recognition based on Acceleration Data

Recognizing Driving Behavior and Road Anomaly Using Smartphone Sensors

An Experimental Comparison Between Seven Classification Algorithms for Activity Recognition

Performance Assessment of Feature Detector-Descriptor Combination