Abstract 1:
Dr. M Sanjay
Assistant Professor
Dept. of Electrical Eng
NIT Calicut
Kerala India 673601
Email: msanjay@nitc.ac.in
Chapter 12
BRAIN COMPUTER INTERFACE
MuhamedJishad T K1 and M Sanjay1
1
Department of Electrical Engg, National Institute of Technology Calicut Kerala India
ABSTRACT
Brain Computer Interface (BCI) is a system of software and hardware that establishes a direct
connection between brain and external devices by making use of signals acquired from the brain.
In principle, any type of brain imaging techniques such as Electroencephalography (EEG),
Magnetoencephalography (MEG), Functional magnetic resonance imaging (fMRI), Functional
near-infrared imaging (fNIR) etc. can be used to deviseBCI systems. However, the neuroelectric
signals such as EEG are the widely studied signals for BCI applications due to their simplicity,
low cost and improved response time when compared to other signal types.The invasive
recording of neuroelectric signals like Electrocorticography (ECoG) and Intracortical Neuron
Recording (INR) can provide better quality signals than scalp recorded EEG, but with a cost of
greater risk to the individual. Additionally, in hybrid BCI systems various other bio signals such
as EMG, ECG etc. are also utilized in addition to the signals directly acquired from the brain in
order to enhance the performance of the system. Signal acquisition, feature extraction, feature
translation, and output device are the primary components of a BCI system.In general, a BCI
system detects and extracts features of the brain signal in order to identify the user’s intentions
and translate it into useful commands to devices that carries out the desired task. BCI systems
find applications mainly in clinical field to rehabilitate people with neuromuscular diseases.
Also, efforts are being made to integrate BCI with multimedia, entertainment and gaming
applications.Even though there are many successful BCI applications developed by several
research groups, this field is still in its infant stage and continuing effort is required to make the
systemmore practical for human use.
The purpose of this chapter is to give an overview of various types of BCI systems, their working
and applications by giving emphasis to signal acquisition, feature extraction and classification
methods. The chapter beginswith an introduction to various neuroimaging techniques that are
relevant to BCI applications. Even though the BCI applications of fMRI, fNRI, MEG etc. are
reviewed in this chapter, the main focus will be on EEG based BCIs which are more popular
among researchers due to the simplicity of application. The chapter further explores theBCI
systems based on different modalities of EEG such as Slow Cortical Potential (SCP),
Sensorimotor Rhythms (SMR), P300 event related potentials and Steady State Visual Evoked
Potentials (SSVEPs)with the help of existing literature. Techniques to improve signal quality and
feature extraction methods are alsoreviewed in this chapter. Then commonly used data classifiers
or classification algorithmsare discussed, which is another important element of BCI that
determines the user’s intention by classifying the features extracted from signals acquired.The
chapter further looks in to the possibilities of hybrid BCI systems.Case studies of significant
works and milestones in BCI research over the years are presented in between the topicsfor
better understanding.The chapter is concluded with an analysis of the state of the art and future
trends inBCI research.
**********
Note: interested for more chapters
Abstract 2:
Dr. K. A. Venkatesh, Professor
Myanmar Institute of Information Technology(MIIT)
Mandalay, Myanmar
Email: ka_venkatesh@miit.edu.mm>
Efficient classification algorithms for Bio signal processing using EEG
Sherly Maria
Research Scholar
Dept. of Computer Science
Christ(Deemed to be University)
Dr. Chandra J.
Assistant Professor
Dept. of Computer Science
Christ(Deemed to be University)
sherly.maria@res.christuniversity.inchandra.j@christuniversity.in
Key Words: EEG, Stress, Signal Processing, Feature Extraction, Classification.
EEG has been widely used to understand brain signals mainly for its non-invasive method and
high accuracy. Signal processing techniques have been used to understand what these signals
mean and to predict occurrence of diseases and in return could be prevented. The aim of this
chapter is to discuss various signal processing techniques for EEG signals in order to interpret
the EEG readings.
Stress has been prevalent in humans since a long time. With increasing complexity in our work
life, the stress is only rising exponentially. Since a lot of light is now being shed on it, there is a
lot of studies going on studying the causes of stress and how to reduce it. EEG is one of the noninvasive methods to record brain waves and one of the most accurate. In order to understand
what the signals mean; classification has to be done. Classification plays a major role in
understanding what the waves mean, it helps preventing potential diseases from occurring by
detecting the diseases like epilepsy and Alzheimer’s. In order to classify these signals
effectively, it is important to select a proper classification algorithm based on the data available.
There are different methods of classification that could be used, like parametric, non-parametric,
time-frequency methods.
This chapter would mainly focus on why signal processing is required and most commonly used
signal processing techniques for classification. The basic understanding of these models and an
explanation of how it is used in signal processing. Among classification, the most commonly
used classifiers like Linear discriminant analysis, SVM, ANN, Deep learning would be covered
in detail with their uses, advantages and disadvantages. This would help researchers understand
the classifiers and models better and would enable them to choose the right model or classifier
for their research accordingly based on the requirements and the kind of data they use. Signal
processing in EEG has always been a complex task. It is however very important for the
researchers to be able to choose the right tool that will enable them to come to the right
conclusion.
Abstract 3, 4 and 5
Dr. K. A. Venkatesh, Professor
Myanmar Institute of Information Technology (MIIT)
Mandalay, Myanmar
Email: ka_venkatesh@miit.edu.mm>
Chapter Proposal for Emerging Trends in Brain and Behavioral Science
Proposed Chapter: Auditory Processing &Computational Brain
Author: Aravind Kumar Rajasekaran, Associate Professor,Department of Speech
Pathology & Audiology,National Institute of Mental Health and Neurosciences,
Bangalore and
K A Venkatesh Professor MIIT, Mandalay, Myanmar(Adjunct Professor, IIIT
BANGALORE, INDIA).
Abstract:Speech the verbal mode of communication sets humans well apart from other
beings and surely formed the strong basis on which today’s civilizationis built upon.
Development of speech (output) wholly depend on the hearing (input). Hearing (Auditory)
domain performs a very challenging task of processing of the input sound, as the stimuli per
se is transient and dynamic. Cochlea the sensory end organ of hearing feeds the electrical
information to the auditory nerve, which in turn connect to the central auditory pathway
reaching the cortex. The inputs undergo transformations at multiple levels of the brainstem
& and the cortex for various outcomes satisfying specific purposes. The auditory pathway
terminates at the temporal lobes, which is rightly termed us the ‘computational hub’. The
temporal lobe employs efficient computational mechanisms to decode the signal for its
physical properties (frequency & intensity) in time domain and then transfers the output to
other related brain areas for the final interpretation and storage.
This chapter has six sections. The first section introduces the concept of the Central auditory
pathway & and central auditory processing, the second section discusses various methods to
study auditory processing, section three deals with Brain stem & auditory processing,
section four talks on Auditory cortical processing, section five deal withNeural Networks and
computational models in auditory processing and final section seven is on Brain computer
interface- Deciphering the input before output.
Keywords:Auditory processing, auditory neuroscience, computational models, brain
computer interface.
Approximate pages : 25-38
Abstract 4:
Chapter Proposal for Brain and Behavior Computing
Proposed Chapter:Role of Statistics in Brain Computing and Behavioral Science
Author:K A Venkatesh Professor MIIT, Mandalay, Myanmar(Adjunct Professor, IIIT
BANGALORE, INDIA).
Abstract: A Brain Computer Interface (BCI) also known as Brain Machine interface.
BMI, is a collection of hardware bundled with software systems that enables human
to interact with their environment using control signals generated by
electroencephalographic activity. In BCI, the most important task is feature
selection and selection from the six popular neuroimaging system, irrespective of
exogenous, endogenous, Synchronous and Asynchronous BCI.
Every thinking process result in an different patterns of the brain signals. Hence,
BCI can be viewed as pattern recognition system. Brain signals will have different
information in each activities. Different channels capture these signals. The
foremost aspect of feature extraction is dimensions reduction then classification and
finally statistical analysis. This chapter focuses on statistical analysis and it contains
five sections. First section is to on Null Hypothesis testing, section 2 is on two
groups t test, section 3 is on one way anova, section 4 is on correlation and
regression, section 5 is on factorial anova.
Keywords: Testing of Hypothesis, correlation and regression, anova
Approximate pages: 25-30
Abstract 5:
Chapter Proposal for Brain and Behavior Computing
Proposed Chapter:Role of Statistics in Brain Computing and Behavioral Science
Author:K A Venkatesh Professor MIIT, Mandalay, Myanmar(Adjunct Professor, IIIT
BANGALORE, INDIA).
Abstract: A Brain Computer Interface (BCI) also known as Brain Machine interface.
BMI, is a collection of hardware bundled with software systems that enables human
to interact with their environment using control signals generated by
electroencephalographic activity. In BCI, the most important task is feature
selection and selection from the six popular neuroimaging system, irrespective of
exogenous, endogenous, Synchronous and Asynchronous BCI.
Every thinking process result in an different patterns of the brain signals. Hence,
BCI can be viewed as pattern recognition system. Brain signals will have different
information in each activities. Different channels capture these signals. The
foremost aspect of feature extraction is dimensions reduction then classification and
finally statistical analysis. This chapter focuses on statistical analysis and it contains
five sections. First section is to on Null Hypothesis testing, section 2 is on two
groups t test, section 3 is on one way anova, section 4 is on correlation and
regression, section 5 is on factorial anova.
Keywords: Testing of Hypothesis, correlation and regression, anova
Abstract 6:
Chapter Proposal for Brain and Behavior Computing
Proposed Chapter: Introduction and Data Acquisition
Author: K. A. Venkatesh Professor MIIT, Myanmar and Anandi Giridharan Principal
Research Scientist, Indian Institute of Science, Bangalore
Abstract:
Enormous increase in big data, learning analytics, and Edge Intelligence
presents new technique to redesign user centric adaptive learning. In this
chapter we are proposing to design next generation adaptive learning
systems based on new developments in learning and technology.
Outline of the chapter: This chapter has five sections: first section
briefly discusses about Informal framework of Adaptive learning
architecture. The second chapter focuses on proper use of data
acquisition methods in adaptive teaching environment. The third, sections
presents techniques of user centric knowledge construction, Section four
shows that students find adaptive learning contents for students better
learning strategies.
Key words: Data acquisition, Adaptive learning, edge intelligence, Learner
model
Abstract 7:
Dr.K C Raveendranathan <indran@ieee.org>
Chapter 4
Data Science in Brain Signal Computing
Abstract
The model of the human brainas a massive, Artificial Neural Network (ANN) is quite popular
among several researchers and in many disciplines of science. It is apparent to note that the
human brain is a massive data processor which involves many signals-biological, and external
data inputs from several sources. It is estimated that an average the human brain contains 86
Billion (86×1012) neurons and this depicts the complexity of the system. It is interesting that the
biosignals emanating from the brain, the Electro-Encephalo Gram (EEG) signals,are of the order
of 2µV to 100µV at 0.5Hz to 100Hz. In contrast to this, there also exists the evoked potential of
the human brain in response to external visual and auditory stimuli which are termed as Evoked
Potential/Event-Related Potential (EP/RP). These are usually of the order of 0.1µV to 20µV at a
frequency of 1Hz to 3kHz. We in this chapter attempts to study in details the processing of Brain
Signals in the context of brain and Human-ComputerInterface. We show that the signals emanate
from the brain are tantamount to typical Data Science conditions, by way of its statistical nature
and volume. For medical applications needle electrodes and implanted electrodes are used to
sense the EEG. On the other hand, applications involving Human-Computer Interface (HCI) use
surface electrodes only. One of the major problems associated with surface electrodes is that the
measured signal will be a superposition of several signals from many neurons. Usually, the
processing of brain signals involves preprocessing to eliminate the noise and artefacts present in
it. Digital processing of EEG signals also involves the usage of suitable Digital-to-Analogue
Converters (ADCs) and Digital Signal Processors. One has to use intelligent signal processing
algorithms to analyse the EEG signals properly. It is well known that different brain regions are
used for performing different tasks or functions such as psychomotor functions, emotional
engagements, sensory perceptions, and visual processing. Thus, the measured signal from the
skull will reflectthese spatially distributed functions of the brain, and always a combined signal.
Over the past two decades, the signal processing hardware and software has matured and
improved immensely and become very inexpensive. Very effective, computationally intensive
signal processing algorithms can now be implemented on Field Programmable Gate Arrays
(FPGAs) and low-priced signal processors. A recent innovation in technology is the biologically
inspired computing, which intent to mimic the functioning of the human brain. To design and
develop systems that are true substitutes to the brain, one has to do pattern recognition from the
sensed signal and data. The foremost step to pattern recognition is feature extraction. The
extracted features of the signals are used to train a model data set either in a supervised or
unsupervised fashion. Thus we have two models of machine learning-the unsupervised and the
supervised. To effective control the learning rate, one has to resort to feature selection techniques
as part of pattern recognition. Thus it is imperative that the brain signal processing involved
principles of Data Science as well.
Abstract 8:
Dr.Arun Sasidharan1, KusumikaKrori Dutta2
arun.s@axxonet.net, kusumika@msrit.edu
1Senior
2Assistant
Research officer & Research Program Head,Axxonet Brain Research Laboratory (ABRL), Bengaluru
Professor, Department of electrical and Electronics Engg, M.S.Ramaiah Institute of Technology, Bengaluru.
MACHINE LEARNING TECHNIQUES FOR BRAIN AND BEHAVIOUR
MEASUREMENTS USING ELECTROENCEPHALOGRAPHY
Abstract
Brain is often considered a highly advanced prediction machine that accomplishes this enormous
feat through complex interaction with its billions of cells called neurons. Due to their inherent
electrical nature, such neuronal activities appear as blurred electrical field changes over scalp.
These can be measured non-invasively as voltage changes using extremely sensitive instruments
called Electroencephalography (EEG). Thus, EEG patterns capture many aspects of mental
processes like cognition, behaviour, and emotions, across the illness to wellness spectrum.
Though several years of training would enable clinicians to visually interpret important features
of EEG signals, these are extremely cumbersome, and the majority of the patterns get discarded
as noise for simplicity. Over the years, several signal processing techniques have helped to
extract many such EEG features that were invisible to even the most experienced eyes. This has
allowed researchers to expand the applications of EEG into many more areas, which may be
broadly categorized into Brain decoding (like detecting behaviour and emotion) and Anomaly
detection (like identifying Alzheimer’s disease, Seizure, Sleep stages). However, the above
applications can involve enormous amounts of EEG features, making the task overtly daunting.
This is where machine learning (ML) techniques have become crucial. These are basically
algorithms involving complex mathematical concepts and programming, which performs a
specified function with the given data and progressively gets better over time. Thus complex
tasks can become efficient and scalable through automation. This allows considerable
improvement in EEG interpretation in terms of accuracy and interpretability for clinical use, as
well as usability for online applications like brain computer interface (BCI). There are several
ML techniques (like K-nearest neighbour, support vector machine, random forest, etc.) as well as
their more evolved form called deep learning techniques (like convolutional neural networks,
recurrent neural networks, etc.) that are commonly used by researchers for more accurate
interpretation of EEG signals, despite their random, non-linear, non-periodic and noise-prone
nature. This chapter will initially provide an overview to the various ML and deep learning
algorithms for multiclass, time series classification from EEG data. Subsequently, it will guide
the readers through the application of ML, using seizure detection and sleep stage detection as
examples. The chapter will stress on how to select EEG features based on neurobiological
grounding and how to decide on an appropriate ML pipeline. Though the main focus of this
chapter is ML application on EEG signals, the same concepts will be applicable in several other
time series data (like fMRI BOLD signals, audio signals, etc.).
Abstract 9:
An efficient single trial classification approach for Devanagari script
based P300 speller using knowledge distillation and transfer learning
Ghanahshyam B. Kshirsagar, Narendra D. Londhe* Department of Electrical Engineering,
National Institute of Technology Raipur, Chhattisgarh, India, 492010.
*corresponding author- nlondhe.ele@nitrr.ac.in
Abstract: Background: In existing Devanagari script based P300 speller (DS-P3S), deep CNN
and weighted ensemble of deep CNN had implemented to improve the performance of
classification of P300 from multiple and single trial respectively. However, DCNN that too its
ensemble variant becomes cumbersome and increases the computational burden of a system due
to large number of trainable parameters. Hence, a spatial-temporal based CNN had implemented
to reduce this computational burden. However, the compressing the deep model also leads to loss
of information which further costs accuracy. Method: Therefore, to compensate this loss, we are
proposing first time the utilization of knowledge distillation to implement a compressed CNN
with comparable performance. The proposed work includes following contribution:1) an
efficient compact model for single trial P300 detection, 2) channel-wise convolution to provide
sparse connectivity, 3) knowledge distillation to compensate the loss and reduce the trade-off
between accuracy and number of trainable parameters, and 4) transfer learning to handle intertrial and inter-subject variability. For experimentation, we have used a self-generated DS dataset
which includes ERP-EEG of 79 characters acquired from 10 health subjects. Results: An average
accuracy of 90.82%±1.47 and 88.74%±2.08 are achieved for cross and within subject
classification in single trial using proposed compact model. There is a positive increment of 24% in average accuracy with reduction of trainable parameters by 3-54 times than existing
DCNN, shallow and compact CNN. This remarkable performance ensures the effectiveness of
knowledge distillation and transfer learning in the implementation of a compact classification
model for DS-P.
Abstract 10:
Md. Hedayetul Islam Shovona*, D (Nanda)
Nandagopal ,Jia Tina Du , and Vijayalakshmi Ramasamya,b
a
a
b
a,
School of Information Technology and Mathematical Sciences,
University of South Australia, Adelaide, Australia
shomy004@mymail.unisa.edu.au
Miami University, Oxford, Ohio 45056, United States of America
*Corresponding author
Directed Functional Brain Networks: Charactisation of information flow direction during
cognitive function using Non-linear Granger Causality
Human Information search and information retrieval is a cognitive function which applies some
level of cognitive load on the user. With the phenomenal growth in affordable mobile devices
the need for complex information in day to day life has been increasing significantly. This has an
impact not only on the user’s information search behavior but also on the cognitive load. The
measurement of dynamic and global interactions of the brain regions during cognitive activity is
a real challenge. Therefore, in this chapter we examine the dynamic behavior of functional
brain networks during both visual and web search tasks. The impact of information search and
retrieval on cognitive function using EEG based functional brain networks is studied. Very few
previous studies have considered the changing dynamics of information flow direction in the
human functional brain network during cognitive function. This novel study is aiming to explore
the information flow direction during cognitive activities. Direct measure of brain activity during
information search is an interesting but challenging topic for information retrieval researchers.
Directed functional brain networks were constructed using nonlinear Granger causality (GC)
from multichannel Electro Encephalogram (EEG) data collected during eyes open ( baseline),
visual search and Web search tasks. The constructed functional brain networks were analyzed
using complex network metrics including connectivity density, clustering coefficient and local
information flow measures as well as our proposed weighted Information Flow Direction
Pattern algorithm. The empirical analysis suggests that increased cognitive activity takes place
in the occipital and frontal lobes during visual and Web search phases compared to baseline
state. The significant increase in both the connectivity densities and clustering coefficients
during the search tasks clearly demonstrate higher cognitive activity in the brain demonstrating
increased cognitive load. The information flow patterns together with the sensitivity of the
complex network metrics to cognitive states observed in this study have potential applications
in examining the effects of cognitive abilities on information search processes. The results of
quantitative measures of cognitive load during both visual and web search tasks demonstrated
in this chapter may contribute towards designing better Information search and retrieval
systems leading to least cognitive/mental load on the user and thus improving the search and
retrievalbehaviors’.
Keywords: Granger Causality, information flow, directed functional brain network, EEG,
cognitive activity, visual search, web search.
Abstract 11:
From : Dr. Dr. Mohan Awasthy
Principal
G H Raisoni Institute of Engineering and Technology Nagpur
BE Electronics, M. Tech Computer Technology
Ph.D. Electronics and Telecommunication
Sr. Member of IEEE
Email: awasthymohan@gmail.com
Study of Neuro Fuzzy System for Human Emotion
This Chapter explain the rational structure and field of study of cognitive intelligent system
(CIS) or Affective Computing and its importance on Emotional State Recognition. In recent
years study of Emotional Intelligence has become a widespread research domain. Several
research has been conducted to create machines that can sense and understand human affective
states, such as emotions, behavior, mood and the interests as well. It tries to fill the
communication gap between human and computers with the sense of feelings. The work is based
on a comprehensive literature survey and referential study of Emotional Intelligence which
illustrates its connection with emotional states recognition domain
Abstract 12:
Dr. Rahul Upadhyay
Post-Doctoral, Trinity College Dublin, Ireland
Assistant Professor
Electronics and Communication Engineering Department
Thapar Institute of Engineering & Technology
(Deemed to be University)
Patiala, Punjab
India
Mobile No.: +91-9755130755
E-mail: rahul.upadhyay@thapar.edu, rupadhya@tcd.ie
Abstract 13:
Komal Jindal, Rahul Upadhyay, Hari Shankar Singh Department of Electronics and Communication
Engineering, Thapar Institute of Engineering and Technology, Patiala 147 004, Punjab, India
Email: komaljindal24@gmail.com, rahul.upadhyay@thapar.edu, harishankar1990@gmail.com
Application of TQWT-Entropy based Automated Epileptic Seizure Identification
An epileptic seizure is a group of disorders caused by a synchronous aberrant discharge of neurons in
the human brain. Early epileptic seizure detection using a Computer-Aided Diagnosis scheme may help
in improving the physiological condition of patients. In this work, an Electroencephalogram (EEG)
activity-based automated epileptic seizure detection scheme is proposed using appropriate Tunable QWavelet Transform (TQWT)-Entropy features. The TQWT can reliably represent the significant
arrangement of constraint time-frequency subbands from considered EEG activity. The proposed
methodology begins with the decomposition of given EEG activity into six time-frequency sub-bands
using TQWT. In the next step, four Entropy features are extracted from decomposed time-frequency
sub-bands. For classification of the extracted features, three machine learning procedures i.e., SVM, RF
and NN are employed. Classification results are evaluated corresponding to extracted feature vectors,
and it is observed that classification accuracy of 100% (using SVM), 99.66% (using ANN) and 98.33%
(using RF) is attained using only single Entropy feature. It is observed that the proposed TQWT-Entropy
based epileptic seizure detection methodology shows significant classification results in terms of
classification accuracy, sensitivity, and specificity. Among all other classifier algorithms, the SVM
classifier emerges as the most suitable classifier algorithm in present work.
Abstract 14:
Evolutionary Comparison with Performance Analysis of Metaheuristic Optimal
Controller for Arc Load
Rajkumar Jhapte1, Anish Pratap Vishwakarma2
1Department
2Department
of EEE, SSTC, SSGI, Bhilai, India
of EE, National Institute of Technology, Meghalaya, India
1jhapte02@gmail.com
2anishvishwakarma9766@gmail.com
Abstract. In recent decades, the randomly variable loads such as Speed variant drives, Modern Power electronics loads,
automated machineries and some high transient current carrying loads such as Arc furnace etc are proved as a
responsible factor for increase in Harmonics and cause of circuit parameter disturbance results in declining the power
quality of system. With this regards has approach towards minimization of disturbing parameter and to enhance the
performance of Optimization Technique for controller based filters is done here. Various novel optimal control
techniques like Genetic Algorithm (GA), Particle swarm optimization (PSO), Gravitational Search Algorithm (GSA),
Harmony Search Algorithm (HSA) and Cuckoo Search Algorithm (CSA) for tuning the parameter of PI Controller are
utilized throughout these work to approaching towards system stability. The main focus of the work is to extract the
reference current after acquiring the load current to the controller which is in our case taken as a non linear current from
an arc furnace load. The control strategies for evaluate reference current is Synchronous Reference frame (SRF) Theory
among various topology available. This industrial load is taken into consideration only after the relevant V-I
characteristics supports the real time operating status of Electric Arc Furnace Load (Chaotic model simulated). The
proposed system is implemented with adoptability and in contrast the employability of metaheuristic optimization
techniques are being observed and simulated to meet the actual disturbing effects of non linear loads as Electric Arc
furnace. Also the ISE, IAE and ITAE criteria are have been implemented to refine the absolute error so that the
optimization itself being more deliberate towards the harmonic minimization. Moreover the reduced percentage of THD
is obtained with ITAE criteria. All the result are simulated in MATLAB 2017a Simulink environment.
Chapter 15:
Monali Gulhane
Research Scholar KL University,Vijayawada,India
Asst Prof JIT,Nagpur ,Maharashtra,India
monali.gulhane4@gmail.com
9766709211/9075089800
Chapter No 10
Machine Learning Techniques for Brain and Behavior
Computing
Author1: Monali Gulhane Author2: T.Sajana
Abstract
Modern understanding of human actions is focused primarily on universal
characteristics of visual images. In recent decades, conventional human activity
identification has been largely focused on the global characteristics of an image,
introducing various static features such as edge features, shape features, statistical
features or transforming features to characterize human behavior. This human
behavior are examined with maximum accuracy in the area of Machine Learning.
Machine learning is an application of artificial intelligence (AI) that provides syste
ms intelligent to automatically learn and develop from experience of data received
and process without being any specifically programmed .This application of
artificial intelligence (AI) plays an vital role in human behavior prediction .Hence
the chapter provides brief knowledge about the techniques of brain computing and
human behavior analysis through machine learning.
The learning cycle begins with experiences or evidence, such as facts, direct knowl
edge, or feedback, to search for correlations in the data and make informed choices
in the future based on the facts we have. The primary objective is to allow comput
ers to automatically learn without human involvement or assistance, and to modify
behavior accordingly. There are numerous techniques to examine the brain activity
and human behavior in machine learning but in this chapter we will focus on deep
learning techniques for brain computing because deep learning has efficient
advantages for brain interface and analysis . Many BCI (Brain Computer
Interfaces) have implemented deep learning techniques for brain computing due to
its advantages that its not time consuming in preprocessing with all feature
engineering steps i.e. it starts working directly on raw data signals of brain to learn
the distinguish information of the back propagation data.
For human behavior computing analyzing the motions or actions of all body
parameter(hands,eyes,legs,heads etc) are important in human behavior prediction
or computing hence for this purpose also deep learning engine of machine learning
is in boom of Artificial Intelligence. In earlier day the storage of data in computer
and performance of computer system restricted the advancement of deep learning
in bioparameter analysis but now a day the use of high end computer system and
cloud are helping to store large amount of data which inherited the development of
deep learning in human behavior computing and analysis . The chapter also gives
and idea of type of deep models called as Deep Neural Networks (DNNs).
According to the research DNNs can directly work on raw inputs to automate the
functions . The chapter also describes the user behavior using actions, activities,
and intra- and inter-activity behavior. Researcher Aitor Almeida and Gorka
Azkune, have created a deep learning architecture based on long short-term
memory networks (LSTMs) that models the inter-activity behavior. This chapter
will give you brief of machine learning usage ,techniques for brain and human
behavior computing with its merits and demerits along with brief knowledge of
human behavior prediction algorithm.
Chapter Flow:
1. Introduction to Machine Learning(Definition Approaches ) (700 words)
2. Machine Learning Algorithm for Brain Computing(Introduction Paragraph)
(1500 words)
2.1 Working of Brain and its data computing process
2.2 Brain Interface Computing:(Definition, Classification)
2.3 Power and Limitations of Algorithm for Brain Computing
3. Machine Learning Algorithm for Human Behavior Computing (Introduction
Paragraph) (1500 words)
3.1 Elements of User Behavior
3.2 Deep neural networks with models