Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Problem Given the number of times in which an unknown event has happened and failed: Required the chance that the probability of its happening in a single trial lies somewhere between any two degrees of probability that can be named. SECTION 1 Definition 1. Several events are inconsistent, when if one of them happens, none of the rest can. 2. Two events are contrary when one, or other of them must; and both together cannot happen. 3. An event is said to fail, when it cannot happen; or, which comes to the same thing, when its contrary has happened. 4. An event is said to be determined when it has either happened or failed. 5. The probability of any event is the ratio between the value at which an expectation depending on the happening of the event ought to be computed, and the value of the thing expected upon it’s 2 happening.

An essay towards solving a problem in the doctrine of chances. [Facsimil]

An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces

Considering the entire BCI system, a big challenge is that information can be extracted from brain signals in a meaningful way. Therefore, most BCI studies are focused on brain signal processing, i...

Determination of Effective Signal Processing Stages for Brain Computer Interface on BCI Competition IV Data Set 2b: A Review Study

Hilbert transform-based event-related patterns for motor imagery brain computer interface

The aim of this paper is to classify both (left and right) hand motor imagery (MI) tasks using event-related (de) synchronization (ERD/ERS) patterns based on the inter trial variance (IV). In this work, publicly available online BCI-2003 competition data set was used which contains MI based Electroencephalography (EEG) data of a single subject. The raw MI based EEG signals were extracted from C3 and C4 channels and band pass filtered to mu (8–12 Hz) frequency band. The mean, inter trial variance and ERD/ERS patterns were calculated in both the channels for both MI tasks of the subject. The patterns were used as feature vector and input to the different machine learning classifiers. The logistic regression and Naive Bayesian classifiers were used for the classification of both MI tasks of the subject. For logistic regression classifier, the accuracy, precision and sensitivity were found to be 92.40%, 92.00% and 92.00%, respectively. Meanwhile, 92.19% accuracy, 92.00% precision and 92.00% sensitivity were obtained for the Naive Bayesian classifier. The logistic regression classifier provides maximum classification accuracy (92.40%) in comparison with the existing methods (87.90–90.20%). Result shows that, the proposed method can quantify ERD/ERS patterns effectively in both the channels and also classifies both MI tasks of the subject successfully.

Classification of Motor Imagery Tasks Using Inter Trial Variance In The Brain Computer Interface

In recent decades, motor imagery (MI) based brain-computer interface (BCI) is served as a control system or rehabilitation tool for motor disabled people. But it has limited applications because of its lower classification performance (classification accuracy, Cohen’s kappa coefficient and etc.). The performance depends on the feature extraction techniques and extraction of relevant features from the brain is challenging task. The existing techniques have low classification performance and are computationally inefficient. This paper introduces phase space reconstruction (PSR) to detect various MI activities and improve the performance of the system. First, raw signals were decomposed into multiple frequency sub-bands using filter bank technique. Second, PSR was applied to each sub-band and dynamical behavior of the brain activities has been analyzed. The optimal parameters (time delay and embedding dimension) of PSR were calculated by average mutual information (AMI) and false nearest neighbors (FNN) methods. The time delay and embedding dimension extracted significant features related to MI activities. The significant features were fed into multi-class support vector machine (SVM) and performance of the classifier was evaluated. The performance of the system is based on classification accuracy (%CA) and Cohen’s kappa coefficient (K). The proposed algorithm and classifier were tested on BCI competition-2005, MI dataset-III-a. The results show that the proposed technique increases the classification accuracy by 3.7% and achieved higher performance (%CA = 89.20% and K= 0.85).

Improving The Performance of Motor Imagery Based Brain-Computer Interface Using Phase Space Reconstruction

Motor imagery (MI) based brain computer interface (BCI) is an assistive device for the motor disabled people. But it has limited applications due to its lower classification performance. To enhance the performance, this paper introduces an efficient method for the detection of both left and right hand MI tasks of the subject using phase space reconstruction (PSR) and empirical mode decomposition (EMD). First, EMD was employed on MI-EEG signals to obtain a set of band limited functions called as intrinsic mode functions (IMFs). To study the MI activities, the IMFs whose main frequency lies between 8– 30 Hz (i.e. sensorimotor frequency band) were selected. On the other hand, PSR was applied to the selected IMFs followed by the extraction of MI features. At last, the significant features (pi0.05) extracted from one-way analysis of variance (ANOVA) were fed into different machine learning models such as logistic regression (LR), Naive Bayes (NB) and support vector machine (SVM) to detect MI tasks. The proposed method and the classifiers were tested on BCI competition 2003 MI dataset. The results show that the SVM improved the classification accuracy upto 4.27% with better performance (i.e. % CA=96.67%, K=0.93 and Auc=0.96) and outperformed the existing methods reported in the literature (maximum % CA=92.40%).

Classification of Motor Imagery Tasks using Phase Space Reconstruction and Empirical Mode Decomposition

In recent decades, motor imagery (MI) based brain computer interface (BCI) is frequently used to control the external devices, but it has limited applications because of its lower classification accuracy (CA). The classification accuracy depends on the feature extraction techniques which is a challenging problem in the field of MI based BCI systems. In this paper, an efficient feature extraction and classification technique is proposed for the detection of multi-class (left hand, right hand, tongue and foot) MI movements with improved classification accuracy. The proposed phase space reconstruction (PSR) technique extracted efficient features during MI activities and the support vector machine (SVM) classifier was used to classify multi-class MI movements. The proposed technique and the classifier are tested on BCI competition-III (2005) dataset-IIIa which contains four-class of MI movements of the subjects. The results showed that the proposed technique improved the classification accuracy of MI signal and has better performance (%CA = 80.86% and Cohen’s kappa coefficient (K)= 0.72) compared to several state-of-the-art techniques.

Niraj Bagh

Papers

Hilbert transform-based event-related patterns for motor imagery brain computer interface

Classification of Motor Imagery Tasks Using Inter Trial Variance In The Brain Computer Interface

Improving The Performance of Motor Imagery Based Brain-Computer Interface Using Phase Space Reconstruction

Classification of Motor Imagery Tasks using Phase Space Reconstruction and Empirical Mode Decomposition

Detection of Motor Imagery Movements Based on the Features of Phase Space Reconstruction