Trial EEG Research Articles

Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks

We studied the reactivity of EEG rhythms (mu rhythms) in association with the imagination of right hand, left hand, foot, and tongue movement with 60 EEG electrodes in nine able-bodied subjects. During hand motor imagery, the hand mu rhythm blocked or desynchronized in all subjects, whereas an enhancement of the hand area mu rhythm was observed during foot or tongue motor imagery in the majority of the subjects. The frequency of the most reactive components was 11.7 Hz ± 0.4 (mean ± SD). While the desynchronized components were broad banded and centered at 10.9 Hz ± 0.9, the synchronized components were narrow banded and displayed higher frequencies at 12.0 Hz ± 1.0. The discrimination between the four motor imagery tasks based on classification of single EEG trials improved when, in addition to event-related desynchronization (ERD), event-related synchronization (ERS) patterns were induced in at least one or two tasks. This implies that such EEG phenomena may be utilized in a multi-class brain–computer interface (BCI) operated simply by motor imagery.

Single-trial lambda wave identification using a fuzzy inference system and predictive statistical diagnosis**Informed consent was obtained from all the subjects whose data are included in this paper and their rights are protected.

The aim of the study was to automate the identification of a saccade-related visual evoked potential (EP) called the lambda wave. The lambda waves were extracted from single trials of electroencephalogram (EEG) waveforms using independent component analysis (ICA). A trial was a set of EEG waveforms recorded from 64 scalp electrode locations while a saccade was performed.Forty saccade-related EEG trials (recorded from four normal subjects) were used in the study. The number of waveforms per trial was reduced from 64 to 22 by pre-processing. The application of ICA to the resulting waveforms produced 880 components (i.e. 4 subjects × 10 trials per subject × 22 components per trial). The components were divided into 373 lambda and 507 nonlambda waves by visual inspection and then they were represented by one spatial and two temporal features. The classification performance of a Bayesian approach called predictive statistical diagnosis (PSD) was compared with that of a fuzzy logic approach called a fuzzy inference system (FIS). The outputs from the two classification approaches were then combined and the resulting discrimination accuracy was evaluated. For each approach, half the data from the lambda and nonlambda wave categories were used to determine the operating parameters of the classification schemes while the rest (i.e. the validation set) were used to evaluate their classification accuracies.The sensitivity and specificity values when the classification approaches were applied to the lambda wave validation data set were as follows: for the PSD 92.51% and 91.73% respectively, for the FIS 95.72% and 89.76% respectively, and for the combined FIS and PSD approach 97.33% and 97.24% respectively (classification threshold was 0.5). The devised signal processing techniques together with the classification approaches provided for an effective extraction and classification of the single-trial lambda waves. However, as only four subjects were included, it will be valuable to further evaluate the methods on a larger group of subjects.

Computerized processing of EEG–EOG–EMG artifacts for multi-centric studies in EEG oscillations and event-related potentials

The aim of this study is to present a package including standard software for the electroencephalographic (EEG), electro-oculographic (EOG) and electromyographic (EMG) preliminary data analysis, which may be suitable to standardize the results of many EEG research centers studies (i.e. multi-centric studies) especially focused on event-related potentials. In particular, our software package includes (semi)automatic procedures for (i) EOG artifact detection and correction, (ii) EMG analysis, (iii) EEG artifact analysis, (iv) optimization of the ratio between artifact-free EEG channels and trials to be rejected. The performances of the software package on EOG–EEG–EMG data related to cognitive–motor tasks were evaluated with respect to the preliminary data analysis performed by two expert electroencephalographists (gold standard). Due to its extreme importance for multi-centric EEG studies, we compared the performances of two representative ‘regression’ methods for the EOG correction in time and frequency domains. The aim was the selection of the most suitable method in the perspective of a multi-centric EEG study. The results showed an acceptable agreement of approximately 95% between the human and software behaviors, for the detection of vertical and horizontal EOG artifacts, the measurement of hand EMG responses for a cognitive–motor paradigm, the detection of involuntary mirror movements, and the detection of EEG artifacts. Furthermore, our results indicated a particular reliability of a ‘regression’ EOG correction method operating in time domain (i.e. ordinary least squares). These results suggest that such a software package could be used for multi-centric EEG studies.

Time-frequency microstructure of event-related electro-encephalogram desynchronisation and synchronisation.

A new method is presented for the analysis of event-related EEG phenomena, in particular event related desynchronisation (ERD) and event related synchronisation (ERS) related to a voluntary movement; the method offers: high time-frequency resolution and, hence, increased ERD/ERS sensitivity (especially in the gamma band, where improvement can exceed an order of magnitude); the ability to analyse the whole picture of energy changes at once, without setting a priori the analysed frequency bands; and a parametric description of the signal's structures. The main idea is based upon averaging energy distributions of single EEG trials in the time-frequency plane. As the estimator for the signal's energy density, matching pursuit is chosen, with stochastic Gabor dictionaries. Other possible estimates are presented on a simulated signal and discussed briefly. The consistency of the results with previous findings is evaluated on the data from a classical voluntary finger movement experiment.

Visually guided motor imagery activates sensorimotor areas in humans

Stimulus-related changes in ongoing electroencephalography (EEG) over sensorimotor areas were investigated during a visually cued motor imagery task. Four subjects were instructed to imagine one-sided hand movements in response to visual cue stimuli. The EEG was recorded from central areas using 27 electrodes set at distances of 2.5 cm. The method of common spatial filters was used to extract discriminatory information of EEG patterns recorded during the two motor imagery conditions. Single EEG trials were classified in intervals of 250 ms for a 8-s period starting 3 s prior to stimulus presentation. The results suggest that perception of the visual cue stimulus modifies oscillations in sensorimotor areas specific to the indicated hand starting as soon as 250–500 ms after stimulus onset.

Adaptive autoregressive modeling used for single-trial EEG classification.

An adaptive autoregressive (AAR) model is used for analyzing event-related EEG changes. Such an AAR model is applied to single EEG trials of three subjects, recorded over both sensorimotor areas during imagination of left and right hand movements. It is found that discrimination between both types of motor-imagery is possible using linear discriminant analysis, but the time point for optimal classification is different in each subject. For the estimation of the AAR parameters, the Least-mean-squares and the Recursive-least-squares algorithms are compared. In both methods, the update coefficient plays a key role: it determines the adaptation ratio as well as the estimation accuracy. A new method, based on minimizing the prediction error, is introduced for determining the update coefficient.

Spike detection. I. Correlation and reliability of human experts

A panel of 5 experienced electroencephalographers detected spikes in EEG trials from 40 epilepsy patients and 10 control subjects. 1952 spikes were detected, and detailed attribute scores were recorded. Statistics from the theory of measurement error are utilized to quantify the reliability and difficulty of the study. An extension of the Pearson correlation coefficient, called the detection correlation coefficient, is derived and used in recognition of the fact that the readers agree on numerous non-spike regions. Spike perception is modeled with both dichotomous and continuous values. As expected, the study reliability is higher when using the continuous values. Standard sensitivity and specificity definitions are extended and applied to continuous-valued spike perception. A database of “panel scores” was created from the 5 reader scorings by merging spikes within 75 msec on each side. The average inter-reader correlation is 0.79 with a corresponding reliability of 0.95. Average spike attributes are calculated, and the resulting database can serve as a “gold standard” for testing computer algorithms or other readers.

PS-13-1 Early contingent negative variation (CNV) is small in a somatosensory paradigm

A novel discriminant method, termed local discriminative spatial patterns (LDSP), is proposed for movement-related potentials (MRPs)-based single-trial electroencephalogram (EEG) classification. Different from conventional discriminative spatial patterns (DSP), LDSP explicitly considers local structure of EEG trials in the construction of scatter matrices in the Fisher-like criterion. The underlying manifold structure of two-dimensional spatio-temporal EEG signals contains more discriminative information. LDSP is an extension to DSP in the sense that DSP can be formulated as a special case of LDSP. By constructing an adjacency matrix, LDSP is calculated as a generalized eigenvalue problem, and so is computationally straightforward. Experiments on MRPs-based single-trial EEG classification show the effectiveness of the proposed LDSP method.

Visualization of sensorimotor areas involved in preparation for hand movement based on classification of μ and central β rhythms in single EEG trials in man

It is well known that μ and central β rhythms start to desynchronize > 1 s before active hand or finger movement. To investigate whether the same cortical areas are involved in desynchronization of μ and central β rhythms, 56-channel EEG recordings were made during right- and left-finger flexions in three normal subjects. The event-related desynchronization (ERD) was quantified in single EEG trials and classified by the Distinction Sensitive Learning Vector Quantization (DSLVQ) algorithm. This DSLVQ selects the most relevant features (electrode positions) for discrimination between the preparatory state for left- and right-finger movements. It was found that the most important electrode positions were close to the primary hand area. However, in all three subjects the focus of the central β ERD was slightly anterior to the focus of μ desynchronization. This can be interpreted that different neural networks are involved in the generation of μ and central β rhythms.

Differentiation between finger, toe and tongue movement in man based on 40 Hz EEG

Movements of right and left index fingers, right toe and tongue were studied by EEG measurement in the alpha and gamma (30–40 Hz) bands. The EEG was recorded with a 56-electrode array over pre- and postcentral areas. For each movement the average power decrease, as a measurment of the event-related desynchronization or power increase in narrow frequency bands, was calculated. Single-trial data from 8 electrodes, 3 frequency bands and 4 time points within a 1 sec window were subject to a classification task. It was found that, based on single EEG trials, the data from the 4 movements could be differentiated with an accuracy of 70% when alpha and gamma band activity were used but only with 58% in the case of the alpha band activity alone. This shows that the gamma band activity or 40 Hz EEG is strongly related to planning of a specific movement and therefore, improves the accuracy of classification significantly.

Topographical display and interpretation of event-related desynchronization during a visual-verbal task

Multichannel EEG recordings were performed during a visual-verbal judgement task with a verbal response using an event-related paradigm. EEG trials of 7 sec were first digitally band pass filtered (10-12 Hz, 10-11 Hz, 11-12 Hz), and the amplitudes were then squared and averaged over all trials. This processing method results in a time course of alpha power and allows us to quantify the event-related desynchronization (ERD) in each EEG channel and to compute series of ERD maps in intervals of 125 msec. Analyzing the 10-11 Hz band, it was possible to study the time course and topographical pattern of cortical activation during visual encoding and cognition. Analysis of the 11-12 Hz band has enabled us, for the first time, to study the time course, spatial localization and extent of activation of speech, premotor and motor areas. We found that the speech centers were activated maximally 250 to 375 msec before speech onset and the SMA about 250 msec before speech onset. The results are preliminary, but demonstrate how much information can be extracted from the scalp EEG.

Graphical display and statistical evaluation of event-related desynchronization (ERD)

A new method which enables quantification of event-related desynchronization (ERD) by measuring changes in the power of rhythmic activity within the alpha band during sensory stimulation and during the interstimulus interval of a CNV paradigm is described. For the processing, EEG trials recorded under the same stimulus conditions and with eyes closed are used. For the power changes, the statistical significance is tested with a non-parametric test. With the proposed method it is possible to measure very brief power changes of the order of 21% (corresponding to amplitude changes of 11%). An ERD may thus be observed in a restricted cortical region.