EEG-Based Detection of Brisk Walking Motor Imagery Using Feature Transformation Techniques

2018 ◽  
pp. 78-89
Author(s):  
Batala Sandhya ◽  
Manjunatha Mahadevappa
Keyword(s):  
Motor Imagery ◽  
Brisk Walking ◽  
Feature Transformation ◽  
Transformation Techniques

scholarly journals Survey on Feature Transformation Techniques for Data Streams

2020 ◽  
Author(s):  
Maroua Bahri ◽  
Albert Bifet ◽  
Silviu Maniu ◽  
Heitor Murilo Gomes
Keyword(s):  
Machine Learning ◽  
Data Streams ◽  
Large Data ◽  
High Dimensional ◽  
Feature Transformation ◽  
Transformation Techniques ◽  
Computational Costs ◽  
The Past ◽  
Fundamental Challenge ◽  
Mining Algorithms

Mining high-dimensional data streams poses a fundamental challenge to machine learning as the presence of high numbers of attributes can remarkably degrade any mining task's performance. In the past several years, dimension reduction (DR) approaches have been successfully applied for different purposes (e.g., visualization). Due to their high-computational costs and numerous passes over large data, these approaches pose a hindrance when processing infinite data streams that are potentially high-dimensional. The latter increases the resource-usage of algorithms that could suffer from the curse of dimensionality. To cope with these issues, some techniques for incremental DR have been proposed. In this paper, we provide a survey on reduction approaches designed to handle data streams and highlight the key benefits of using these approaches for stream mining algorithms.

Biometric template security: an overview

Sensor Review ◽  
2018 ◽  
Vol 38 (1) ◽  
pp. 120-127 ◽  
Author(s):  
Naveed Riaz ◽  
Ayesha Riaz ◽  
Sajid Ali Khan
Keyword(s):  
Provable Security ◽  
Feature Transformation ◽  
Transformation Techniques ◽  
Content Type ◽  
General Acceptance ◽  
Biometric Security ◽  
Template Security ◽  
Biometric Template ◽  
High Level ◽  
Biometric Templates

Purpose The security of the stored biometric template is itself a challenge. Feature transformation techniques and biometric cryptosystems are used to address the concerns and improve the general acceptance of biometrics. The purpose of this paper is to provide an overview of different techniques and processes for securing the biometric templates. Furthermore, the paper explores current research trends in this area. Design/methodology/approach In this paper, the authors provide an overview and survey of different features transformation techniques and biometric cryptosystems. Findings Feature transformation techniques and biometric cryptosystems provide reliable biometric security at a high level. There are many techniques that provide provable security with practical viable recognition rates. However, there remain several issues and challenges that are being faced during the deployment of these technologies. Originality/value This paper provides an overview of currently used techniques for securing biometric templates and also outlines the related issues and challenges.

Detection of motor imagery of brisk walking from electroencephalogram

2015 ◽  
Vol 244 ◽  
pp. 33-44 ◽  
Author(s):  
Huijuan Yang ◽  
Cuntai Guan ◽  
Chuan Chu Wang ◽  
Kai Keng Ang
Keyword(s):  
Motor Imagery ◽  
Brisk Walking

scholarly journals Performance tuning for machine learning-based software development effort prediction models

2019 ◽  
pp. 1308-1324
Author(s):  
EGEMEN ERTUĞRUL ◽  
ZAKİR BAYTAR ◽  
ÇAĞATAY ÇATAL ◽  
ÖMER CAN MURATLI
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Principal Component ◽  
Parameter Tuning ◽  
Development Effort ◽  
Feature Transformation ◽  
Transformation Techniques ◽  
Software Development Effort ◽  
Effort Prediction

Software development effort estimation is a critical activity of the project management process. In this study, machine learning algorithms were investigated in conjunction with feature transformation, feature selection, and parameter tuning techniques to estimate the development effort accurately and a new model was proposed as part of an expert system. We preferred the most general-purpose algorithms, applied parameter optimization technique (Grid- Search), feature transformation techniques (binning and one-hot-encoding), and feature selection algorithm (principal component analysis). All the models were trained on the ISBSG datasets and implemented by using the scikit-learn package in the Python language. The proposed model uses a multilayer perceptron as its underlying algorithm, applies binning of the features to transform continuous features and one-hot-encoding technique to transform categorical data into numerical values as feature transformation techniques, does feature selection based on the principal component analysis method, and performs parameter tuning based on the GridSearch algorithm. We demonstrate that our effort prediction model mostly outperforms the other existing models in terms of prediction accuracy based on the mean absolute residual parameter.

scholarly journals Convolutional Neural Network and Feature Transformation for Distant Speech Recognition

2018 ◽  
Vol 8 (6) ◽  
pp. 5381
Author(s):  
Hilman F. Pardede ◽  
Asri R. Yuliani ◽  
Rika Sustika
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Deep Neural Network ◽  
Hidden Markov ◽  
Feature Learning ◽  
Learning Technologies ◽  
Feature Transformation ◽  
Local Correlation ◽  
Transformation Techniques ◽  
Mel Frequency Cepstral Coefficient

In many applications, speech recognition must operate in conditions where there are some distances between speakers and the microphones. This is called distant speech recognition (DSR). In this condition, speech recognition must deal with reverberation. Nowadays, deep learning technologies are becoming the the main technologies for speech recognition. Deep Neural Network (DNN) in hybrid with Hidden Markov Model (HMM) is the commonly used architecture. However, this system is still not robust against reverberation. Previous studies use Convolutional Neural Networks (CNN), which is a variation of neural network, to improve the robustness of speech recognition against noise. CNN has the properties of pooling which is used to find local correlation between neighboring dimensions in the features. With this property, CNN could be used as feature learning emphasizing the information on neighboring frames. In this study we use CNN to deal with reverberation. We also propose to use feature transformation techniques: linear discriminat analysis (LDA) and maximum likelihood linear transformation (MLLT), on mel frequency cepstral coefficient (MFCC) before feeding them to CNN. We argue that transforming features could produce more discriminative features for CNN, and hence improve the robustness of speech recognition against reverberation. Our evaluations on Meeting Recorder Digits (MRD) subset of Aurora-5 database confirm that the use of LDA and MLLT transformations improve the robustness of speech recognition. It is better by 20% relative error reduction on compared to a standard DNN based speech recognition using the same number of hidden layers.

The loading of motor imagery

2006 ◽  
Author(s):  
Andrew B. Slifkin
Keyword(s):  
Motor Imagery

Experience Influences Brain Mechanisms of Watching Dance

2011 ◽  
Vol 29 (supplement) ◽  
pp. 352-377 ◽  
Author(s):  
Seon Hee Jang ◽  
Frank E Pollick
Keyword(s):  
Motor Imagery ◽  
Visual Experience ◽  
Primary Motor Cortex ◽  
Action Observation ◽  
Brain Area ◽  
Retrosplenial Cortex ◽  
Imagery Task ◽  
Temporoparietal Junction ◽  
Action Observation Network ◽  
Viewing Experience

The study of dance has been helpful to advance our understanding of how human brain networks of action observation are influenced by experience. However previous studies have not examined the effect of extensive visual experience alone: for example, an art critic or dance fan who has a rich experience of watching dance but negligible experience performing dance. To explore the effect of pure visual experience we performed a single experiment using functional Magnetic Resonance Imaging (fMRI) to compare the neural processing of dance actions in 3 groups: a) 14 ballet dancers, b) 10 experienced viewers, c) 12 novices without any extensive dance or viewing experience. Each of the 36 participants viewed short 2-second displays of ballet derived from motion capture of a professional ballerina. These displays represented the ballerina as only points of light at the major joints. We wished to study the action observation network broadly and thus included two different types of display and two different tasks for participants to perform. The two different displays were: a) brief movies of a ballet action and b) frames from the ballet movies with the points of lights connected by lines to show a ballet posture. The two different tasks were: a) passively observe the display and b) imagine performing the action depicted in the display. The two levels of display and task were combined factorially to produce four experimental conditions (observe movie, observe posture, motor imagery of movie, motor imagery of posture). The set of stimuli used in the experiment are available for download after this paper. A random effects ANOVA was performed on brain activity and an effect of experience was obtained in seven different brain areas including: right Temporoparietal Junction (TPJ), left Retrosplenial Cortex (RSC), right Primary Somatosensory Cortex (S1), bilateral Primary Motor Cortex (M1), right Orbitofrontal Cortex (OFC), right Temporal Pole (TP). The patterns of activation were plotted in each of these areas (TPJ, RSC, S1, M1, OFC, TP) to investigate more closely how the effect of experience changed across these areas. For this analysis, novices were treated as baseline and the relative effect of experience examined in the dancer and experienced viewer groups. Interpretation of these results suggests that both visual and motor experience appear equivalent in producing more extensive early processing of dance actions in early stages of representation (TPJ and RSC) and we hypothesise that this could be due to the involvement of autobiographical memory processes. The pattern of results found for dancers in S1 and M1 suggest that their perception of dance actions are enhanced by embodied processes. For example, the S1 results are consistent with claims that this brain area shows mirror properties. The pattern of results found for the experienced viewers in OFC and TP suggests that their perception of dance actions are enhanced by cognitive processes. For example, involving aspects of social cognition and hedonic processing – the experienced viewers find the motor imagery task more pleasant and have richer connections of dance to social memory. While aspects of our interpretation are speculative the core results clearly show common and distinct aspects of how viewing experience and physical experience shape brain responses to watching dance.

Addicted to Christ

2018 ◽  
Author(s):  
Helena Hansen
Keyword(s):  
Cultural Anthropology ◽  
Domestic Space ◽  
Spiritual Power ◽  
Transformation Techniques ◽  
Industrial Culture ◽  
Depth Analysis ◽  
Alternative Masculinities ◽  
Street Corner ◽  
Post Industrial ◽  
The U.S

How are spiritual power and self-transformation cultivated in street ministries? This book provides an in-depth analysis of Pentecostal ministries in Puerto Rico that were founded and run by self-identified “ex-addicts,” ministries that are also widespread in poor Black and Latino neighborhoods in the U.S. mainland. The book melds cultural anthropology and psychiatry. Through the stories of ministry converts, the book examines key elements of Pentecostalism: mysticism, ascetic practice, and the idea of other-worldliness. It then reconstructs the ministries' strategies of spiritual victory over addiction: transformation techniques to build spiritual strength and authority through pain and discipline; cultivation of alternative masculinities based on male converts' reclamation of domestic space; and radical rupture from a post-industrial “culture of disposability.” By contrasting the ministries' logic of addiction with that of biomedicine, the book rethinks roads to recovery, discovering unexpected convergences with biomedicine while revealing the allure of street corner ministries.

Simultaneous Classification of Multiple Motor Imagery and P300 for Increase in Output Information of Brain-computer Interface

2013 ◽  
Vol 133 (3) ◽  
pp. 635-641
Author(s):  
Genzo Naito ◽  
Lui Yoshida ◽  
Takashi Numata ◽  
Yutaro Ogawa ◽  
Kiyoshi Kotani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Output Information
Sign in / Sign up

Export Citation Format

Share Document