Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement:A Preliminary Study on the Implementation of eConnectome in Motor Imagery Protocols

Introduction . Sensorimotor cortex is activated similarly during motor execution and motor imagery. The study of functional connectivity networks (FCNs) aims at successfully modeling the dynamics of information flow between cortical areas. Materials and Methods . Seven healthy subjects performed 4 motor tasks (real foot, imaginary foot, real hand, and imaginary hand movements), while electroencephalography was recorded over the sensorimotor cortex. Event-Related Desynchronization/Synchronization (ERD/ERS) of the mu-rhythm was used to evaluate MI performance. Source detection and FCNs were studied with eConnectome. Results and Discussion . Four subjects produced similar ERD/ERS patterns between motor execution and imagery during both hand and foot tasks, 2 subjects only during hand tasks, and 1 subject only during foot tasks. All subjects showed the expected brain activation in well-performed MI tasks, facilitating cortical source estimation. Preliminary functional connectivity analysis shows formation of networks on the sensorimotor cortex during motor imagery and execution. Conclusions . Cortex activation maps depict sensorimotor cortex activation, while similar functional connectivity networks are formed in the sensorimotor cortex both during actual and imaginary movements. eConnectome is demonstrated as an effective tool for the study of cortex activation and FCN. The implementation of FCN in motor imagery could induce promising advancements in Brain Computer Interfaces

Disentangling somatosensory evoked potentials of the fingers: limitations and clinical potential. Raw data

In searching for predictors for recovery of upper limb function post stroke we studied reproducibility of somatosensory potentials (SEP) evoked by finger stimulation in healthy subjects. The modality that we used was Electroencephalography(EEG)

GamEMO: How Physiological Signals Show your Emotions and Enhance your Game Experience

The proposed demonstration is an automatic emotion assessment installation used for game's dynamic difficulty adjustment. The goal of the system is to maintain the player of the game in a state of entertainment and engagement where his/her skills match the difficulty level of the game. The player's physiological signals are recorded while playing a Tetris game and signal processing, feature extraction and classification techniques are applied to the signals in order to detect when the player is anxious or bored. The level of the Tetris game is then adjusted according to the player's detected emotional state. The demonstration will also serve as an experimental protocol to test the player's experience through their interaction with the proposed platform

Spatial resolution for EEG source reconstruction—A simulation study on SEPs

Background: The accuracy of source reconstruction depends on the spatial configuration of the neural sources underlying encephalographic signals, the temporal distance of the source activity, the level and structure of noise in the recordings, and – of course – on the employed inverse method. This plenitude of factors renders a definition of ‘spatial resolution’ of the electro-encephalogram (EEG) a challenge. New method: A proper definition of spatial resolution requires a ground truth. We used data from numerical simulations of two dipoles changed with waveforms resembling somatosensory evoked potentials peaking at 20, 30, 50, 100 ms. We varied inter-dipole distances and added noise to the simulated scalp recordings with distinct signal-to-noise ratios (SNRs). Prior to inverse modeling we pre-whitened the simulated data and the leadfield. We tested a two-dipole fit, sc-MUSIC, and sc-eLORETA and assessed their accuracy via the distance between the simulated and estimated sources. Results: To quantify the spatial resolution of EEG, we introduced the notion of separability, i.e. the separation of two dipolar sources with a certain inter-dipole distance. Our results indicate separability of two sources in the presence of realistic noise with SNR up to 3 if they are 11 mm or further apart. Comparison with existing methods: In the presence of realistic noise, spatial pre-whitening appears mandatory preprocessing step irrespective of the inverse method employed. Conclusions: Separability is a legitimate measure to quantify EEG's spatial resolution. An optimal resolution in source reconstruction requires spatial pre-whitening as a crucial pre-processing step

Disentangling somatosensory evoked potentials of the fingers: Limitations and clinical potential

In searching for clinical biomarkers of the somatosensory function, we studied reproducibility of somatosensory potentials (SEP) evoked by finger stimulation in healthy subjects. SEPs induced by electrical stimulation and especially after median nerve stimulation is a method widely used in the literature. It is unclear, however, if the EEG recordings after finger stimulation are reproducible within the same subject. We tested in five healthy subjects the consistency and reproducibility of responses through bootstrapping as well as test–retest recordings. We further evaluated the possibility to discriminate activity of different fingers both at electrode and at source level. The lack of consistency and reproducibility suggest responses to finger stimulation to be unreliable, even with reasonably high signal-to-noise ratio and adequate number of trials. At sources level, somatotopic arrangement of the fingers representation was only found in one of the subjects. Although finding distinct locations of the different fingers activation was possible, our protocol did not allow for non-overlapping dipole representations of the fingers. We conclude that despite its theoretical advantages, we cannot recommend the use of somatosensory potentials evoked by finger stimulation to extract clinical biomarkers.Biomechatronics & Human-Machine Contro

Determination of head conductivity frequency response in vivo with optimized EIT-EEG

Electroencephalography (EEG) benefits from accurate head models. Dipole source modelling errors can be reduced from over 1 cm to a few millimetres by replacing generic head geometry and conductivity with tailored ones. When adequate head geometry is available, electrical impedance tomography (EIT) can be used to infer the conductivities of head tissues. In this study, the boundary element method (BEM) is applied with three-compartment (scalp, skull and brain) subject-specific head models.The optimal injection of small currents to the head with a modular EIT current injector, and voltage measurement by an EEG amplifier is first sought by simulations. The measurement with a 64-electrode EEG layout is studied with respect to three noise sources affecting EIT: background EEG, deviations from the fitting assumption of equal scalp and brain conductivities, and smooth model geometry deviations from the true head geometry. The noise source effects were investigated depending on the positioning of the injection and extraction electrode and the number of their combinations used sequentially.The deviation from equal scalp and brain conductivities produces rather deterministic errors in the three conductivities irrespective of the current injection locations. With a realistic measurement of around 2 min and around 8 distant distinct current injection pairs, the error from the other noise sources is reduced to around 10% or less in the skull conductivity. The analysis of subsequent real measurements, however, suggests that there could be subject-specific local thinnings in the skull, which could amplify the conductivity fitting errors. With proper analysis of multiplexed sinusoidal EIT current injections, the measurements on average yielded conductivities of 340 mS/m (scalp and brain) and 6.6 mS/m (skull) at 2 Hz. From 11 to 127 Hz, the conductivities increased by 1.6% (scalp and brain) and 6.7% (skull) on the average. The proper analysis was ensured by using recombination of the current injections into virtual ones, avoiding problems in location-specific skull morphology variations.The observed large intersubject variations support the need for in vivo measurement of skull conductivity, resulting in calibrated subject-specific head models

Disentangling Somatosensory Evoked Potentials of the Fingers:Limitations and Clinical Potential

In searching for clinical biomarkers of the somatosensory function, we studied reproducibility of somatosensory potentials (SEP) evoked by finger stimulation in healthy subjects. SEPs induced by electrical stimulation and especially after median nerve stimulation is a method widely used in the literature. It is unclear, however, if the EEG recordings after finger stimulation are reproducible within the same subject. We tested in five healthy subjects the consistency and reproducibility of responses through bootstrapping as well as test–retest recordings. We further evaluated the possibility to discriminate activity of different fingers both at electrode and at source level. The lack of consistency and reproducibility suggest responses to finger stimulation to be unreliable, even with reasonably high signal-to-noise ratio and adequate number of trials. At sources level, somatotopic arrangement of the fingers representation was only found in one of the subjects. Although finding distinct locations of the different fingers activation was possible, our protocol did not allow for non-overlapping dipole representations of the fingers. We conclude that despite its theoretical advantages, we cannot recommend the use of somatosensory potentials evoked by finger stimulation to extract clinical biomarkers