Functional principal components analysis by choice of norm
Journal of Multivariate Analysis
Learning and Classification of Complex Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov models for online classification of single trial EEG data
Pattern Recognition Letters
Neural network classification of word evoked neuromagnetic brain activity
Emergent neural computational architectures based on neuroscience
An improved sequential method for principal component analysis
Pattern Recognition Letters
Wavelet methods for continuous-time prediction using Hilbert-valued autoregressive processes
Journal of Multivariate Analysis
Statistical processing of large image sequences
IEEE Transactions on Image Processing
An adaptable time-delay neural-network algorithm for image sequence analysis
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Estimation of a change-point in the mean function of functional data
Journal of Multivariate Analysis
Testing the stability of the functional autoregressive process
Journal of Multivariate Analysis
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We consider the problem of classifying a high-dimensional time series into a number of disjoint classes defined by training data. Techniques of this type are an important component of a number of emerging technologies. These include the use of dense sensor arrays for condition monitoring, brain-computer interfaces for communications and control, the detection of moving pedestrians from sequences of images and the study of cognitive function using high-resolution electroencephalography (EEG). We propose a novel approach to problems of this type using the parameters of an underlying functional auto-regression model. We compare the performance of this approach using two contrasting data sets. The first is based on simulated series with different characteristics and sampling schemes and a second based on high-dimensional times series generated by multi-channel EEG. Both experiments show that our approach outperforms conventional time series methods by exploiting low-intrinsic dimensionality (smoothness). In addition, our simulation experiments show that good performance can be maintained for data generated by non-stationary sampling schemes, the Latter causing large reductions in the performance of conventional procedures. These experiments suggest that meaningful information can be extracted from high-resolution EEG.