Steven Lemm

Bio: Steven Lemm is an academic researcher from Fraunhofer Society. The author has contributed to research in topics: Covariance & Portfolio optimization. The author has an hindex of 13, co-authored 20 publications receiving 2799 citations. Previous affiliations of Steven Lemm include Analysis Group & Fraunhofer Institute for Open Communication Systems.

Spatio-spectral filters for improving the classification of single trial EEG

Abstract: Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, nonstationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements.

BCI competition 2003-data set III: probabilistic modeling of sensorimotor /spl mu/ rhythms for classification of imaginary hand movements

Abstract: Brain-computer interfaces require effective online processing of electroencephalogram (EEG) measurements, e.g., as a part of feedback systems. We present an algorithm for single-trial online classification of imaginary left and right hand movements, based on time-frequency information derived from filtering EEG wideband raw data with causal Morlet wavelets, which are adapted to individual EEG spectra. Since imaginary hand movements lead to perturbations of the ongoing pericentral /spl mu/ rhythm, we estimate probabilistic models for amplitude modulation in lower (10 Hz) and upper (20 Hz) frequency bands over the sensorimotor hand cortices both contra- and ipsilaterally to the imagined movements (i.e., at EEG channels C3 and C4). We use an integrative approach to accumulate over time evidence for the subject's unknown motor intention. Disclosure of test data labels after the competition showed this approach to succeed with an error rate as low as 10.7%.

A novel mechanism for evoked responses in the human brain.

Abstract: Magnetoencephalographic and electroencephalographic evoked responses are primary real-time objective measures of cognitive and perceptual processes in the human brain. Two mechanisms (additive activity and phase reset) have been debated and considered as the only possible explanations for evoked responses. Here we present theoretical and empirical evidence of a third mechanism contributing to the generation of evoked responses. Interestingly, this mechanism can be deduced entirely from the characteristics of spontaneous oscillations in the absence of stimuli. We show that the amplitude fluctuations of neuronal α oscillations at rest are associated with changes in the mean value of ongoing activity in magnetoencephalography, a phenomenon that we term baseline shifts associated with α oscillations. When stimuli modulate the amplitude of α oscillations, baseline shifts become the basis of a novel mechanism for the generation of evoked responses; the averaging of several trials leads to a cancellation of the oscillatory component but the baseline shift remains, which gives rise to an evoked response. We propose that the presence of baseline shifts associated with α oscillations can be explained by the asymmetric flow of inward and outward neuronal currents related to the generation of α oscillations. Our findings are relevant to the vast majority of electroencephalographic and magnetoencephalographic studies involving perceptual, cognitive and motor activity. © The Authors (2007).

An introduction to kernel-based learning algorithms

Abstract: This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.

A review of classification algorithms for EEG-based brain–computer interfaces

Abstract: In this paper we review classification algorithms used to design brain–computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

Shaping functional architecture by oscillatory alpha activity : gating by inhibition

Abstract: In order to understand the working brain as a network, it is essential to identify the mechanisms by which information is gated between regions. We here propose that information is gated by inhibiting task-irrelevant regions, thus routing information to task-relevant regions. The functional inhibition is reflected in oscillatory activity in the alpha band (8-13 Hz). From a physiological perspective the alpha activity provides pulsed inhibition reducing the processing capabilities of a given area. Active processing in the engaged areas is reflected by neuronal synchronization in the gamma band (30-100 Hz) accompanied by an alpha band decrease. According to this framework the brain should be studied as a network by investigating cross-frequency interactions between gamma and alpha activity. Specifically the framework predicts that optimal task performance will correlate with alpha activity in task-irrelevant areas. In this review we will discuss the empirical support for this framework. Given that alpha activity is by far the strongest signal recorded by EEG and MEG, we propose that a major part of the electrophysiological activity detected from the working brain reflects gating by inhibition.

The Boosting Approach to Machine Learning An Overview

Abstract: Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent work on boosting including analyses of AdaBoost’s training error and generalization error; boosting’s connection to game theory and linear programming; the relationship between boosting and logistic regression; extensions of AdaBoost for multiclass classification problems; methods of incorporating human knowledge into boosting; and experimental and applied work using boosting.