86 research outputs found
Can we identify non-stationary dynamics of trial-to-trial variability?"
Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings
Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood
This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way
Sparse multinomial kernel discriminant analysis (sMKDA)
Dimensionality reduction via canonical variate analysis (CVA) is important for pattern recognition and has been extended variously to permit more flexibility, e.g. by "kernelizing" the formulation. This can lead to over-fitting, usually ameliorated by regularization. Here, a method for sparse, multinomial kernel discriminant analysis (sMKDA) is proposed, using a sparse basis to control complexity. It is based on the connection between CVA and least-squares, and uses forward selection via orthogonal least-squares to approximate a basis, generalizing a similar approach for binomial problems. Classification can be performed directly via minimum Mahalanobis distance in the canonical variates. sMKDA achieves state-of-the-art performance in terms of accuracy and sparseness on 11 benchmark datasets
Hierarchic Bayesian models for kernel learning
The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance when compared to that obtained from any single data source. We present a Bayesian hierarchical model which enables kernel learning and present effective variational Bayes estimators for regression and classification. Illustrative experiments demonstrate the utility of the proposed method
Machine learning-based dexterous control of hand prostheses
Upper-limb myoelectric prostheses are controlled by muscle activity information
recorded on the skin surface using electromyography (EMG). Intuitive prosthetic control
can be achieved by deploying statistical and machine learning (ML) tools to decipher
the user’s movement intent from EMG signals. This thesis proposes various
means of advancing the capabilities of non-invasive, ML-based control of myoelectric
hand prostheses. Two main directions are explored, namely classification-based
hand grip selection and proportional finger position control using regression methods.
Several practical aspects are considered with the aim of maximising the clinical
impact of the proposed methodologies, which are evaluated with offline analyses as
well as real-time experiments involving both able-bodied and transradial amputee
participants.
It has been generally accepted that the EMG signal may not always be a reliable
source of control information for prostheses, mainly due to its stochastic and non-stationary
properties. One particular issue associated with the use of surface EMG
signals for upper-extremity myoelectric control is the limb position effect, which is
related to the lack of decoding generalisation under novel arm postures. To address
this challenge, it is proposed to make concurrent use of EMG sensors and inertial
measurement units (IMUs). It is demonstrated this can lead to a significant improvement
in both classification accuracy (CA) and real-time prosthetic control performance.
Additionally, the relationship between surface EMG and inertial measurements is investigated
and it is found that these modalities are partially related due to reflecting
different manifestations of the same underlying phenomenon, that is, the muscular
activity.
In the field of upper-limb myoelectric control, the linear discriminant analysis (LDA)
classifier has arguably been the most popular choice for movement intent decoding.
This is mainly attributable to its ease of implementation, low computational requirements,
and acceptable decoding performance. Nevertheless, this particular method
makes a strong fundamental assumption, that is, data observations from different
classes share a common covariance structure. Although this assumption may often
be violated in practice, it has been found that the performance of the method is
comparable to that of more sophisticated algorithms. In this thesis, it is proposed to
remove this assumption by making use of general class-conditional Gaussian models
and appropriate regularisation to avoid overfitting issues. By performing an exhaustive analysis on benchmark datasets, it is demonstrated that the proposed approach
based on regularised discriminant analysis (RDA) can offer an impressive increase in decoding
accuracy. By combining the use of RDA classification with a novel confidence-based
rejection policy that intends to minimise the rate of unintended hand motions,
it is shown that it is feasible to attain robust myoelectric grip control of a prosthetic
hand by making use of a single pair of surface EMG-IMU sensors.
Most present-day commercial prosthetic hands offer the mechanical abilities to
support individual digit control; however, classification-based methods can only produce
pre-defined grip patterns, a feature which results in prosthesis under-actuation.
Although classification-based grip control can provide a great advantage over conventional
strategies, it is far from being intuitive and natural to the user. A potential
way of approaching the level of dexterity enjoyed by the human hand is via continuous
and individual control of multiple joints. To this end, an exhaustive analysis
is performed on the feasibility of reconstructing multidimensional hand joint angles
from surface EMG signals. A supervised method based on the eigenvalue formulation
of multiple linear regression (MLR) is then proposed to simultaneously reduce the
dimensionality of input and output variables and its performance is compared to that
of typically used unsupervised methods, which may produce suboptimal results in
this context. An experimental paradigm is finally designed to evaluate the efficacy of
the proposed finger position control scheme during real-time prosthesis use.
This thesis provides insight into the capacity of deploying a range of computational
methods for non-invasive myoelectric control. It contributes towards developing
intuitive interfaces for dexterous control of multi-articulated prosthetic hands by
transradial amputees
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