Design of Assistive Speller Machine Based on Brain Computer Interfacing

2017 ◽  
pp. 385-418
Author(s):  
Suryoday Basak
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Signal Processing ◽  
Data Analysis ◽  
Brain Computer Interface ◽  
Classification Methods ◽  
Eeg Signals ◽  
Signal Processing Methods ◽  
Brain Computer Interfacing

Machine Learning (ML) has assumed a central role in data assimilation and data analysis in the last decade. Many methods exist that cater to the different kinds of data centric applications in terms of complexity and domain. Machine Learning methods have been derived from classical Artificial Intelligence (AI) models but are a lot more reliant on statistical methods. However, ML is a lot broader than inferential statistics. Recent advances in computational neuroscience has identified Electroencephalography (EEG) based Brain Computer Interface (BCI) as one of the key agents for a variety of medical and nonmedical applications. However, efficiency in analysing EEG signals is tremendously difficult to achieve because of three reasons: size of data, extent of computation and poor spatial resolution. The book chapter discusses the Machine Learning based methods employed by the author to classify EEG signals for potentials observed based on varying levels of a subject's attention, measured using a NeuroSky Mindwave Mobile. It reports challenges faced in developing BCIs based on available hardware, signal processing methods and classification methods.

scholarly journals FRAMEWORK OF CONTROLLING 3D VIRTUAL HUMAN EMOTIONAL WALKING USING BCI

2015 ◽  
Vol 75 (4) ◽  
Author(s):  
Faris Amin M. Abuhashish ◽  
Hoshang Kolivand ◽  
Mohd Shahrizal Sunar ◽  
Dzulkifli Mohamad
Keyword(s):  
Signal Processing ◽  
Augmented Reality ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Virtual Human ◽  
Eeg Signals ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
New Framework

A Brain-Computer Interface (BCI) is the device that can read and acquire the brain activities. A human body is controlled by Brain-Signals, which considered as a main controller. Furthermore, the human emotions and thoughts will be translated by brain through brain signals and expressed as human mood. This controlling process mainly performed through brain signals, the brain signals is a key component in electroencephalogram (EEG). Based on signal processing the features representing human mood (behavior) could be extracted with emotion as a major feature. This paper proposes a new framework in order to recognize the human inner emotions that have been conducted on the basis of EEG signals using a BCI device controller. This framework go through five steps starting by classifying the brain signal after reading it in order to obtain the emotion, then map the emotion, synchronize the animation of the 3D virtual human, test and evaluate the work. Based on our best knowledge there is no framework for controlling the 3D virtual human. As a result for implementing our framework will enhance the game field of enhancing and controlling the 3D virtual humans’ emotion walking in order to enhance and bring more realistic as well. Commercial games and Augmented Reality systems are possible beneficiaries of this technique.

Functional and QoS Semantics-Driven SOA-Based Biomedical Multimedia Processing

2011 ◽  
pp. 335-359
Author(s):  
Shih-Hsi Liu ◽  
Yu Cao ◽  
Ming Li ◽  
Thell Smith ◽  
John Harris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medical Education ◽  
Feature Extraction ◽  
Data Analysis ◽  
Requirements Elicitation ◽  
Multimedia Data ◽  
Multimedia Processing ◽  
Wide Range ◽  
Processing Component

Although there have existed a wide range of techniques of biomedical multimedia processing, none of them could be generally satisfied by various domains. The main reason for such deficiency is due to the correlative nature between biomedical multimedia data and the techniques applied to them. This book chapter introduces an SOA-based biomedical multimedia infrastructure with a pre-processing component. Such an infrastructure adapts the concepts of requirements elicitation of Software Engineering as well as a training set of Machine Learning to analyze functional and QoS properties of biomedical multimedia data in advance. Such properties will be constructed as ontology and used for selecting the most appropriate services to perform data analysis, transmission, or retrieval. Two medical education projects are introduced as case studies to illustrate the usage of functional and QoS semantics extracted from a feature extraction service to improve the performance of subsequent classification service and searching service, respectively.

Motor Imagery Classification Using EEG Signals for Brain-Computer Interface Applications

2019 ◽  
pp. 241-251
Author(s):  
Subrota Mazumdar ◽  
Rohit Chaudhary ◽  
Suruchi Suruchi ◽  
Suman Mohanty ◽  
Divya Kumari ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Nearest Neighbor ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Test Cases ◽  
Multiple Channels ◽  
Eeg Signals ◽  
Brain Computer Interfacing ◽  
Electroencephalogram Eeg

In this chapter, a nearest neighbor (k-NN)-based method for efficient classification of motor imagery using EEG for brain-computer interfacing (BCI) applications has been proposed. Electroencephalogram (EEG) signals are obtained from multiple channels from brain. These EEG signals are taken as input features and given to the k-NN-based classifier to classify motor imagery. More specifically, the chapter gives an outline of the Berlin brain-computer interface that can be operated with minimal subject change. All the design and simulation works are carried out with MATLAB software. k-NN-based classifier is trained with data from continuous signals of EEG channels. After the network is trained, it is tested with various test cases. Performance of the network is checked in terms of percentage accuracy, which is found to be 99.25%. The result suggested that the proposed method is accurate for BCI applications.

Application of Machine Learning to Analyse Biomedical Signals for Medical Diagnosis

2021 ◽  
pp. 205-236
Author(s):  
Upendra Kumar ◽  
Shashank Yadav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Analysis ◽  
Medical Diagnosis ◽  
Medical Information ◽  
Heart Diseases ◽  
Information Analysis ◽  
Appropriate Treatment ◽  
New Knowledge ◽  
Interest In Research

Interest in research involving health-medical information analysis based on artificial intelligence has recently been increasing. Most of the research in this field has been focused on searching for new knowledge for predicting and diagnosing disease by revealing the relation between disease and various information features of data. However, still needed are more research and interest in applying the latest advanced artificial intelligence-based data analysis techniques to bio-signal data, which are continuous physiological records, such as EEG (electroencephalography) and ECG (electrocardiogram). This study presents a survey of ECG classification into arrhythmia types. Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient.

scholarly journals Machine learning as the new approach in understanding biomarkers of suicidal behavior

2020 ◽  
Author(s):  
Alja Videtič Paska ◽  
Katarina Kouter
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
High Throughput ◽  
Suicidal Behavior ◽  
Technological Development ◽  
Machine Learning Algorithms ◽  
Classical Approach ◽  
New Approach

In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore ‘omic’ studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.

scholarly journals The Collaboration between Hearing Aid Users and Artificial Intelligence to Optimize Sound

2021 ◽  
Vol 42 (03) ◽  
pp. 282-294
Author(s):  
Laura Winther Balling ◽  
Lasse Lohilahti Mølgaard ◽  
Oliver Townend ◽  
Jens Brehm Bagger Nielsen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Signal Processing ◽  
Hearing Aids ◽  
Hearing Aid ◽  
Bayesian Optimization ◽  
Listening Environments ◽  
User Data ◽  
The Individual ◽  
Average User

AbstractHearing aid gain and signal processing are based on assumptions about the average user in the average listening environment, but problems may arise when the individual hearing aid user differs from these assumptions in general or specific ways. This article describes how an artificial intelligence (AI) mechanism that operates continuously on input from the user may alleviate such problems by using a type of machine learning known as Bayesian optimization. The basic AI mechanism is described, and studies showing its effects both in the laboratory and in the field are summarized. A crucial fact about the use of this AI is that it generates large amounts of user data that serve as input for scientific understanding as well as for the development of hearing aids and hearing care. Analyses of users' listening environments based on these data show the distribution of activities and intentions in situations where hearing is challenging. Finally, this article demonstrates how further AI-based analyses of the data can drive development.

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