There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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

Data Mining - Concepts and Techniques.

The organization of behavior: A neuropsychological theory

Motion intention can be detected from human Electroencephalography (EEG) signals through BCI, which can facilitate motor motion control for disabled or paralyzed people. However, the continuous use of BCI is hindered by the non-stationarity of the EEG signals. This paper proposes a method to identify the EEG signal components that can be used to train a classifier to address the non-stationarity issue. The proposed method is based on Transfer Component Analysis (TCA). TCA seeks to locate components that can be transferred across domains in a Reproducing Kernel Hilbert Space (RKHS). The distributions associated with data are closer to each other in the subspaces spanned by the identified transfer components. Therefore, typical machine learning techniques can be applied in the subspace spanned by these transfer components. This results in classifiers that can be trained on the source domain and tested on the target domain. The proposed Stationary Transfer Component Analysis (STCA) method is compared with Stationary Sub-space Analysis (SSA) on the BCI competition IV dataset 2a. The results show significant improvements over the baseline case and the results are better than those produced by SSA.

https://www.actapress.com/PaperInfo.aspx?paperId=454866

Stationary transfer component analysis for brain computer interfacing

Whether transcranial direct current stimulation (tDCS) benefits stroke rehabilitation remains unclear. To investigate how tDCS reorganizes brain circuitry, nineteen post-stroke patients underwent rehabilitation sessions with bi-hemispheric real vs sham tDCS intervention. Resting motor threshold measurements showed tDCS evoked higher excitability in the motor cortex that enhanced the descending conduction from the lesioned primary motor cortex to the target hand muscle. Granger causality analysis further revealed brain circuitry rewiring among the lesioned cerebellum, premotor, and primary motor cortex in the tDCS group compared to the sham owing to the newly formed connections close to the anodal electrode. Rebuilding of these critical pathways was clear via the increase of event related desynchronisation (ERD) and white matter integrity in the same lesioned region. Furthermore, only the tDCS group demonstrated a positive recovery trend in the penumbra regions by the longitudinal functional magnetic resonance imaging (fMRI) analysis. To interpret tDCS mechanism, we introduce a polarized gamma-aminobutyric acid (GABA) theory, where GABAA receptor activity depends on the orientation of dipolar GABA that can be manipulated by tDCS field. Results suggest that tDCS intervention lowers motor excitability via re-orienting GABA, leading to reorganization of the lesioned cortical network, and the motor descending pathway, finally the recovery of motor function.

How transcranial direct current stimulation facilitates post-stroke rehabilitation

This paper proposes a novel active learning method for the classification of motor imagery electroencephalogram (EEG) signals Specifically, we propose an iterative clustering and support vector-based criterion to select samples of high-confidence to construct a robust training set The common spatial pattern (CSP)-based features are iteratively clustered till the number of support vectors in the cluster is less than a predefined threshold A predefined number of samples close to the cluster centers are chosen When such clusters cannot be found, the samples that are of farthest distances to a group of support vectors of class “0” and “1” are alternately chosen Experimental results on BCI competition IV dataset IIb show superior performance compared with a baseline method, which is 9% increase in accuracy averaged across subjects and training sizes

Iterative clustering and support vectors-based high-confidence query selection for motor imagery EEG signals classification

Decoding of directional information in the motor cortex traditionally utilizes only firing rate information. However, information from other features could be extracted and combined with firing rate in order to increase classification accuracy. This study proposes the combination of firing rate and spike-train synchrony information in the decoding of motor cortical activity. Synchrony measures used are Event Synchronization (ES), SPIKE-Distance, and ISI-Distance. All data used for analyses were obtained from implanted electrode recordings of the primary motor cortex of a monkey that was trained to manipulate a motorized vehicle with 4 degrees of freedom (left, right, front and stop) via joystick control. Firstly, synchrony features could decode time periods, which were otherwise incorrectly decoded by firing rate alone, above chance levels. Secondly, using an ensemble classifier design for offline analysis, combining firing rate and ISI-distance information increases overall decoding accuracy by 1.1%. These results show that synchrony features in spike-trains do contain information not carried in firing rate. In addition, these results also demonstrate the feasibility of combining synchrony and firing rate for improving the classification accuracy of invasive brain-machine interface (BMI) in the control of neural prosthetics.

Combining firing rate and spike-train synchrony features in the decoding of motor cortical activity

 Several functional neuroimaging studies had been performed to explore the sensorimotor function for motor imagery and passive movement, but there is scanty work that investigated the cortical activation pattern for passive movement using functional Near-Infrared Spectroscopy (fNIRS). This study investigated the cortical activation pattern from fNIRS data of 8 healthy subjects performing motor imagery and passive movement tasks using a Haptic Knob robot. Group averaged contrasts were defined as motor imagery versus idle and passive movement versus idle. The cortical activations for motor imagery appeared on the contralateral sensorimotor area, whereas the cortical activations for passive movement appeared on both contralateral and ipsilateral sensorimotor area. This result suggests that the performance of passive movement has a wider cortical activation compared to the performance of motor imagery. compared to passive movement

Kai Keng Ang

Papers

Stationary transfer component analysis for brain computer interfacing

How transcranial direct current stimulation facilitates post-stroke rehabilitation

Iterative clustering and support vectors-based high-confidence query selection for motor imagery EEG signals classification

Combining firing rate and spike-train synchrony features in the decoding of motor cortical activity

Cortical activation of passive hand movement using Haptic Knob: a preliminary multi-channel fNIRS study.