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  • Automatic determination of EMG-contaminated components and validation of independent component analysis using EEG during pharmacologic paralysis.

    3 May 2018

    OBJECTIVE: Validate independent component analysis (ICA) for removal of EMG contamination from EEG, and demonstrate a heuristic, based on the gradient of EEG spectra (slope of graph of log EEG power vs log frequency, 7-70 Hz) from paralysed awake humans, to automatically identify and remove components that are predominantly EMG. METHODS: We studied the gradient of EMG-free EEG spectra to quantitatively inform the choice of threshold. Then, pre-existing EEG from 3 disparate experimental groups was examined before and after applying the heuristic to validate that the heuristic preserved neurogenic activity (Berger effect, auditory odd ball, visual and auditory steady state responses). RESULTS: (1) ICA-based EMG removal diminished EMG contamination up to approximately 50 Hz, (2) residual EMG contamination using automatic selection was similar to manual selection, and (3) task-induced cortical activity remained, was enhanced, or was revealed using the ICA-based methodology. CONCLUSION: This study further validates ICA as a powerful technique for separating and removing myogenic signals from EEG. Automatic processing based on spectral gradients to exclude EMG-containing components is a conceptually simple and valid technique. SIGNIFICANCE: This study strengthens ICA as a technique to remove EMG contamination from EEG whilst preserving neurogenic activity to 50 Hz.

  • Surface Laplacian of central scalp electrical signals is insensitive to muscle contamination.

    26 April 2018

    The objective of this paper was to investigate the effects of surface Laplacian processing on gross and persistent electromyographic (EMG) contamination of electroencephalographic (EEG) signals in electrical scalp recordings. We made scalp recordings during passive and active tasks, on awake subjects in the absence and in the presence of complete neuromuscular blockade. Three scalp surface Laplacian estimators were compared to left ear and common average reference (CAR). Contamination was quantified by comparing power after paralysis (brain signal, B) with power before paralysis (brain plus muscle signal, B+M). Brain:Muscle (B:M) ratios for the methods were calculated using B and differences in power after paralysis to represent muscle (M). There were very small power differences after paralysis up to 600 Hz using surface Laplacian transforms (B:M > 6 above 30 Hz in central scalp leads). Scalp surface Laplacian transforms reduce muscle power in central and pericentral leads to less than one sixth of the brain signal, two to three times better signal detection than CAR. Scalp surface Laplacian transformations provide robust estimates for detecting high-frequency (gamma) activity, for assessing electrophysiological correlates of disease, and also for providing a measure of brain electrical activity for use as a standard in the development of brain/muscle signal separation methods.

  • Persistent abnormality detected in the non-ictal electroencephalogram in primary generalised epilepsy.

    26 April 2018

    OBJECTIVES: Gamma oscillations (30-100 Hz gamma electroencephalographic (EEG) activity) correlate with high frequency synchronous rhythmic bursting in assemblies of cerebral neurons participating in aspects of consciousness. Previous studies in a kainic acid animal model of epilepsy revealed increased intensity of gamma rhythms in background EEG preceding epileptiform discharges, leading the authors to test for intensified gamma EEG in humans with epilepsy. METHODS: 64 channel cortical EEG were recorded from 10 people with primary generalised epilepsy, 11 with partial epilepsy, and 20 controls during a quiescent mental state. Using standard methods of EEG analysis the strength of EEG rhythms (fast Fourier transformation) was quantified and the strengths of rhythms in the patient groups compared with with controls by unpaired t test at 1 Hz intervals from 1 Hz to 100 Hz. RESULTS: In patients with generalised epilepsy, there was a threefold to sevenfold increase in power of gamma EEG between 30 Hz and 100 Hz (p<0.01). Analysis of three unmedicated patients with primary generalised epilepsies revealed an additional 10-fold narrow band increase of power around 35 Hz-40 Hz (p<0.0001). There were no corresponding changes in patients with partial epilepsy. CONCLUSIONS: Increased gamma EEG is probably a marker of the underlying ion channel or neurotransmitter receptor dysfunction in primary generalised epilepsies and may also be a pathophysiological prerequisite for the development of seizures. The finding provides a new diagnostic approach and also links the pathophysiology of generalised epilepsies to emerging concepts of neuronal correlates of consciousness.

  • Investigating the generators of the scalp recorded visuo-verbal P300 using cortically constrained source localization.

    10 May 2018

    Considerable ambiguity exists about the generators of the scalp recorded P300, despite a vast body of research employing a diverse range of methodologies. Previous investigations employing source localization techniques have been limited largely to equivalent current dipole models, with most studies identifying medial temporal and/or hippocampal sources, but providing little information about the contribution of other cortical regions to the generation of the scalp recorded P3. Event-related potentials (ERPs) were recorded from 5 subjects using a 124-channel sensor array during the performance of a visuo-verbal Oddball task. Cortically constrained, MRI-guided boundary element modeling was used to identify the cortical generators of this target P3 in individual subjects. Cortical generators of the P3 were localized principally to the intraparietal sulcus (IPS) and surrounding superior parietal lobes (SPL) bilaterally in all subjects, though with some variability across subjects. Two subjects also showed activity in the lingual/inferior occipital gyrus and mid-fusiform gyrus. A group cortical surface was calculated by non-linear warping of each subject's segmented cortex followed by averaging and creation of a group mesh. Source activity identified across the group reflected the individual subject activations in the IPS and SPL bilaterally and in the lingual/inferior occipital gyrus primarily on the left. Activation of IPS and SPL is interpreted to reflect the role of this region in working memory and related attention processes and visuo-motor integration. The activity in left lingual/inferior occipital gyrus is taken to reflect activation of regions associated with modality-specific analysis of visual word forms.

  • Multiplication of EEG samples through replicating, biasing, and overlapping

    27 October 2017

    EEG recording is a time consuming operation during which the subject is expected to stay still for a long time performing tasks. It is reasonable to expect some fluctuation in the level of focus toward the performed task during the task period. This study is focused on investigating various approaches for emphasizing regions of interest during the task period. Dividing the task period into three segments of beginning, middle and end, is expectable to improve the overall classification performance by changing the concentration of the training samples toward regions in which subject had better concentration toward the performed tasks. This issue is investigated through the use of techniques such as i) replication, ii) biasing, and iii) overlapping. A dataset with 4 motor imagery tasks (BCI Competition III dataset IIIa) is used. The results illustrate the existing variations within the potential of different segments of the task period and the feasibility of techniques that focus the training samples toward such regions. © 2012 Springer-Verlag.

  • The impact of PSO based dimension reduction on EEG classification

    15 March 2018

    The high dimensional nature of EEG data due to large electrode numbers and long task periods is one of the main challenges of studying EEG. Evolutionary alternatives to conventional dimension reduction methods exhibit the advantage of not requiring the entire recording sessions for operation. Particle Swarm Optimization (PSO) is an Evolutionary method that achieves performance through evaluation of several generations of possible solutions. This study investigates the feasibility of a 2 layer PSO structure for synchronous reduction of both electrode and task period dimensions using 4 motor imagery EEG data. The results indicate the potential of the proposed PSO paradigm for dimension reduction with insignificant losses in classification and the practical uses in subject transfer applications. © 2012 Springer-Verlag.

  • Multiplying the mileage of your dataset with subwindowing

    10 May 2018

    This study is focused on improving the classification performance of EEG data through the use of some data restructuring methods. In this study, the impact of having more training instances/samples vs. using shorter window sizes is investigated. The BCI2003 IVa dataset is used to examine the results. The results not surprisingly indicate that, up to a certain point, having higher numbers of training instances significantly improves the classification performance while the use of shorter window sizes tends to worsen performance in a way that usually cannot fully be compensated for by the additional instances, but tends to provide useful gain in overall performance for small divisors into two or three subepochs. We have moreover determined that use of an incomplete set of overlapping windows can have little effect, and is inapplicable for the smallest divisors, but that use of overlapping subepochs from three specific non-overlapping areas (start, middle and end) of a superepoch tends to contribute significant additional information. Examination of a division into five equal non-overlapping areas indicates that for some subjects the first or last fifth contributes significantly less information than the middle three fifths. © 2011 Springer-Verlag.

  • Evaluation of a minimum-norm based beamforming technique, sLORETA, for reducing tonic muscle contamination of EEG at sensor level.

    7 May 2018

    BACKGROUND: Cranial and cervical muscle activity (electromyogram, EMG) contaminates the surface electroencephalogram (EEG) from frequencies below 20 through to frequencies above 100Hz. It is not possible to have a reliable measure of cognitive tasks expressed in EEG at gamma-band frequencies until the muscle contamination is removed. NEW METHOD: In the present work, we introduce a new approach of using a minimum-norm based beamforming technique (sLORETA) to reduce tonic muscle contamination at sensor level. Using a generic volume conduction model of the head, which includes three layers (brain, skull, and scalp), and sLORETA, we estimated time-series of sources distributed within the brain and scalp. The sources within the scalp were considered to be muscle and discarded in forward modelling. RESULT: (1) The method reduced EMG contamination, more strongly at peripheral channels; (2) task-induced cortical activity was retained or revealed after removing putative muscle activity. COMPARISON WITH EXISTING METHODS: This approach can decrease tonic muscle contamination in scalp measurements without relying on time-consuming processing of expensive MRI data. In addition, it is competitive to ICA in muscle reduction and can be reliably applied on any length of recorded data that captures the dynamics of the signals of interest. CONCLUSION: This study suggests that sLORETA can be used as a method to quantitate cranial muscle activity and reduce its contamination at sensor level.

  • Investigating a gaze-tracking brain computer interface concept using steady state visually evoked potentials

    27 October 2017

    This project investigated the possibility of a user's gaze being tracked within the area of a computer monitor bounded by multiple light sources, each stimulating an SSVEP. If realised, such a system would allow a spatial arrangement of interactive elements around a screen, rather than the discrete list of commands accessible through existing SSVEP based BCIs. Research-level EEG equipment would make the proposed BCI prohibitively expensive for home users. Thus, investigation was made into the utility of inexpensive consumer-grade EEG equipment, as is available for computer-gaming. SSVEPs were elicited using initially a traditional strobe light source and then a set of individual LEDs, as necessary for the simultaneous stimulation of multiple SSVEPs. Clear responses were recorded using the research EEG system for both the strobe and LED sources; however the consumer system lacked sufficient sensitivity to reliably detect the SSVEPs. Tests with two stimulating LEDs showed that two SSVEPs of differing frequencies be resolved simultaneously, and that the amplitude of the response decreases as the user's gaze is directed further from the stimulating light source. Further work will aim to derive the user's gaze location within an area bounded by multiple stimulating LEDs, using the relative amplitudes of the elicited SSVEPs. © 2012 IEEE.

  • Reducing training requirements through evolutionary based dimension reduction and subject transfer

    26 April 2018

    © 2016 Elsevier B.V. Training Brain Computer Interface (BCI) systems to understand the intention of a subject through Electroencephalogram (EEG) data currently requires multiple training sessions with a subject in order to develop the necessary expertise to distinguish signals for different tasks. Conventionally the task of training the subject is done by introducing a training and calibration stage during which some feedback is presented to the subject. This training session can take several hours which is not appropriate for on-line EEG-based BCI systems. An alternative approach is to use previous recording sessions of the same person or some other subjects that performed the same tasks (subject transfer) for training the classifiers. The main aim of this study is to generate a methodology that allows the use of data from other subjects while reducing the dimensions of the data. The study investigates several possibilities for reducing the necessary training and calibration period in subjects and the classifiers and addresses the impact of i) evolutionary subject transfer and ii) adapting previously trained methods (retraining) using other subjects data. Our results suggest reduction to 40% of target subject data is sufficient for training the classifier. Our results also indicate the superiority of the approaches that incorporated evolutionary subject transfer and highlights the feasibility of adapting a system trained on other subjects.