On optimal spatial filtering for the detection of phase coupling in multivariate neural recordings.

INTRODUCTION: Neuronal oscillations synchronize processing in the brain over large spatiotemporal scales and thereby facilitate integration of individual functional modules. Up to now, the relation between the phases of neuronal oscillations and behavior or perception has mainly been analyzed in sensor space of multivariate EEG/MEG recordings. However, sensor-space analysis distorts the topographies of the underlying neuronal sources and suffers from low signal-to-noise ratio. Instead, we propose an optimized source reconstruction approach (Phase Coupling Optimization, PCO). METHODS: PCO maximizes the 'mean vector length', calculated from the phases of recovered neuronal sources and a target variable of interest (e.g., experimental performance). As pre-processing, the signal-to-noise ratio in the search-space is maximized by spatio-spectral decomposition. PCO was benchmarked against several competing algorithms and sensor-space analysis using realistic forward model simulations. As a practical example, thirteen 96-channel EEG measurements during a simple reaction time task were analyzed. After time-frequency decomposition, PCO was applied to the EEG to examine the relation between the phases of pre-stimulus EEG activity and reaction times. RESULTS: In simulations, PCO outperformed other spatial optimization approaches and sensor-space analysis. Scalp topographies of the underlying source patterns and the relation between the phases of the source activity and the target variable could be reconstructed accurately even for very low SNRs (-10dB). In a simple reaction time experiment, the phases of pre-stimulus delta waves (<0.1Hz) with widely distributed fronto-parietal source topographies were found predictive of the reaction times. DISCUSSION AND CONCLUSIONS: From multivariate recordings, PCO can reconstruct neuronal sources that are phase-coupled to a target variable using a data-driven optimization approach. Its superiority has been shown in simulations and in the analysis of a simple reaction time experiment. From this data, we hypothesize that the phase entrainment of slow delta waves (<1Hz) facilitates sensorimotor integration in the brain and that this mechanism underlies the faster processing of anticipated stimuli. We further propose that the examined slow delta waves, observed to be phase-coupled to reaction times, correspond to the compound potentials typically observed in paradigms of stimulus anticipation and motor preparation.

Neural correlates of lexical decisions in Parkinson's disease revealed with multivariate extraction of cortico-subthalamic interactions.

OBJECTIVE: Neural interactions between cortex and basal ganglia are pivotal for sensorimotor processing. Specifically, coherency between cortex and subthalamic structures is a frequently studied phenomenon in patients with Parkinson's disease. However, it is unknown whether cortico-subthalamic coherency might also relate to cognitive aspects of task performance, e.g., language processing. Furthermore, standard coherency studies are challenged by how to efficiently handle multi-channel recordings. METHODS: In eight patients with Parkinson's disease treated with deep brain stimulation, simultaneous recordings of surface electroencephalography and deep local field potentials were obtained from bilateral subthalamic nuclei, during performing a lexical decision task. A recent multivariate coherency measure (maximized imaginary part of coherency, MIC) was applied, simultaneously accounting for multi-channel recordings. RESULTS: Cortico-subthalamic synchronization (MIC) in 14-35Hz oscillations positively correlated with accuracy in lexical decisions across patients, but not in 7-13Hz oscillations. In contrast to multivariate MIC, no significant correlation was obtained when extracting cortico-subthalamic synchronization by "standard" bivariate coherency. CONCLUSIONS: Cortico-subthalamic synchronization may relate to non-motor aspects of task performance, here reflected in lexical accuracy. SIGNIFICANCE: The results tentatively suggest the relevance of cortico-subthalamic interactions for lexical decisions. Multivariate coherency might be effective to extract neural synchronization from multi-channel recordings.

Correlation between cortical and subcortical neural dynamics on multiple time scales in Parkinson's disease.

Complex amplitude dynamics of dominant alpha oscillations (8-13 Hz) in the cortex can be captured with long-range temporal correlations (LRTC) in healthy subjects and in various diseases. In patients with Parkinson's disease (PD), intra-nuclear coherence was demonstrated in dominant beta rhythms (10-30 Hz) in the basal ganglia. However, so far the relation between cortical LRTC (across tens of seconds) and subcortical coherence (millisecond scale) is unknown. We addressed these "multiscale interactions" by simultaneous recordings of surface electroencephalography (EEG) and deep local field potentials (LFP) from the bilateral subthalamic nucleus (STN) in eight patients with severe PD eligible for deep brain stimulation, who performed a lexical decision task on medication. In the continuous data set LRTC up to 20s were calculated in the amplitude envelope of 8-13-Hz EEG oscillations (across whole scalp), and subcortical coherence was assessed with measures being insensitive to volume conduction artifacts (imaginary part of coherency; iCOH) in 10-20 and 21-30-Hz oscillations in STN-LFP. We showed a significant positive correlation across patients between cortical LRTC (8-13Hz) and subcortical iCOH selectively in 10-20-Hz oscillations in the left STN. Our results suggest a relation between neural dynamics in the most dominant rhythms in the cortex and basal ganglia in PD, extending across multiple time scales (milliseconds vs. tens of seconds). Furthermore, the investigation of multiscale interactions might contribute to our understanding of cortical-subcortical neural coupling in PD.

Primary somatosensory contextual modulation is encoded by oscillation frequency change.

OBJECTIVE: This study characterized thalamo-cortical communication by assessing the effect of context-dependent modulation on the very early somatosensory evoked high-frequency oscillations (HF oscillations). METHODS: We applied electrical stimuli to the median nerve together with an auditory oddball paradigm, presenting standard and deviant target tones representing differential cognitive contexts to the constantly repeated electrical stimulation. Median nerve stimulation without auditory stimulation served as unimodal control. RESULTS: A model consisting of one subcortical (near thalamus) and two cortical (Brodmann areas 1 and 3b) dipolar sources explained the measured HF oscillations. Both at subcortical and the cortical levels HF oscillations were significantly smaller during bimodal (somatosensory plus auditory) than unimodal (somatosensory only) stimulation. A delay differential equation model was developed to investigate interactions within the 3-node thalamo-cortical network. Importantly, a significant change in the eigenfrequency of Brodmann area 3b was related to the context-dependent modulation, while there was no change in the network coupling. CONCLUSION: This model strongly suggests cortico-thalamic feedback from both cortical Brodmann areas 1 and 3b to the thalamus. With the 3-node network model, thalamo-cortical feedback could be described. SIGNIFICANCE: Frequency encoding plays an important role in contextual modulation in the somatosensory thalamo-cortical network.

Ultra-low-noise EEG/MEG systems enable bimodal non-invasive detection of spike-like human somatosensory evoked responses at 1 kHz.

Non-invasive EEG detection of very high frequency somatosensory evoked potentials featuring frequencies up to and above 1 kHz has been recently reported. Here, we establish the detectability of such components by combined low-noise EEG/MEG. We recorded SEP/SEF simultaneously using median nerve stimulation in five healthy human subjects inside an electromagnetically shielded room, combining a low-noise EEG custom-made amplifier (4.7 nV/√Hz) and a custom-made single-channel low-noise MEG (0.5 fT/√Hz @ 1 kHz). Both, low-noise EEG and MEG revealed three spectrally distinct and temporally overlapping evoked components: N20 (<100 Hz), sigma-burst (450-750 Hz), and kappa-burst (850-1200 Hz). The two recording modalities showed similar relative scaling of signal amplitude in all three frequencies domains (EEG [10 nV] ≅ MEG [1 fT]). Pronounced waveform (peak-by-peak) overlap of EEG and MEG signals is observed in the sigma band, whereas in the kappa band overlap was only partial. A decreasing signal-to-noise ratio (SNR; calculated for n = 12.000 averages) from sigma to kappa components characterizes both, electric and magnetic field recordings: Sigma-band SNR was 12.9  ±  5.5/19.8  ±  12.6 for EEG/MEG, and kappa-band SNR at 3.77  ±  0.8/4.5  ±  2.9. High-frequency performance of a tailor-made MEG matches closely with simultaneously recorded low-noise EEG for the non-invasive detection of somatosensory evoked activity at and above 1 kHz. Thus, future multi-channel dual-mode low-noise technology could offer complementary views for source reconstruction of the neural generators underlying such high-frequency responses, and render neural high-frequency processes related to multi-unit spike discharges accessible in non-invasive recordings.

Voluntary control of corticomuscular coherence through neurofeedback: a proof-of-principle study in healthy subjects.

Corticomuscular coherence (CMC) relates to synchronization between activity in the motor cortex and the muscle activity. The strength of CMC can be affected by motor behavior. In a proof-of-principle study, we examined whether independent of motor output parameters, healthy subjects are able to voluntarily modulate CMC in a neurofeedback paradigm. Subjects received visual online feedback of their instantaneous CMC strength, which was calculated between an optimized spatial projection of multichannel electroencephalography (EEG) and electromyography (EMG) in an individually defined target frequency range. The neurofeedback training consisted of either increasing or decreasing CMC strength using a self-chosen mental strategy while performing a simple motor task. Evaluation of instantaneous coherence showed that CMC strength was significantly larger when subjects had to increase than when to decrease CMC; this difference between the two task conditions did not depend on motor performance. The exclusion of confounding factors such as motor performance, attention and task complexity in study design provides evidence that subjects were able to voluntarily modify CMC independent of motor output parameters. Additional analysis further strengthened the assumption that the subjects' response was specifically shaped by the neurofeedback. In perspective, we suggest that CMC-based neurofeedback could provide a therapeutic approach in clinical conditions, such as motor stroke, where CMC is altered.

Interhemispheric functional interactions between the subthalamic nuclei of patients with Parkinson's disease.

Parkinson's disease (PD) is characterized by widespread neural interactions in cortico-basal-ganglia networks primarily in beta oscillations (approx. 10-30 Hz), as suggested by previous findings of levodopa-modulated interhemispheric coherence between the bilateral subthalamic nuclei (STN) in local field potential recordings (LFPs). However, due to confounding effects of volume conduction the existence of 'genuine' interhemispheric subcortical coherence remains an open question. To address this issue we utilized the imaginary part of coherency (iCOH) which, in contrast to the standard coherence, is not susceptible to volume conduction. LFPs were recorded from eight patients with PD during wakeful rest before and after levodopa administration. We demonstrated genuine coherence between the bilateral STN in both 10-20 and 21-30 Hz oscillations, as revealed by a non-zero iCOH. Crucially, increased iCOH in 10-20 Hz oscillations positively correlated with the worsening of motor symptoms in the OFF medication condition across patients, which was not the case for standard coherence. Furthermore, across patients iCOH was increased after levodopa administration in 21-30 Hz oscillations. These results suggest a functional distinction between low and high beta oscillations in STN-LFP in line with previous studies. Furthermore, the observed functional coupling between the bilateral STN might contribute to the understanding of bilateral effects of unilateral deep brain stimulation. In conclusion, the present results imply a significant contribution of time-delayed neural interactions to interhemispheric coherence, and the clinical relevance of long-distance neural interactions between bilateral STN for motor symptoms in PD.

Distinction between added-energy and phase-resetting mechanisms in non-invasively detected somatosensory evoked responses.

Non-invasively recorded averaged event-related potentials (ERP) represent a convenient opportunity to investigate human brain perceptive and cognitive processes. Nevertheless, generative ERP mechanisms are still debated. Two previous approaches have been contested in the past: the added-energy model in which the response raises independently from the ongoing background activity, and the phase-reset model, based on stimulus-driven synchronization of oscillatory ongoing activity. Many criteria for the distinction of these two models have been proposed, but there is no definitive methodology to disentangle them, owing also to the limited information at the single trial level. Here, we propose a new approach combining low-noise EEG technology and multivariate decomposition techniques. We present theoretical analyses based on simulated data and identify in high-frequency somatosensory evoked responses an optimal target for the distinction between the two mechanisms.

Functional and effective connectivity in subthalamic local field potential recordings of patients with Parkinson's disease.

In Parkinson's disease (PD) levodopa-associated changes in the power and long-range temporal correlations of beta oscillations have been demonstrated, yet the presence and modulation of genuine connectivity in local field potentials (LFP) recorded from the subthalamic nucleus (STN) remains an open question. The present study investigated LFP recorded bilaterally from the STN at wakeful rest in ten patients with PD after overnight withdrawal of levodopa (OFF) and after a single dose levodopa administration (ON). We utilized connectivity measures being insensitive to volume conduction (functional connectivity: non-zero imaginary part of coherency; effective connectivity: phase-slope index). We demonstrated the presence of neuronal interactions in the frequency range of 10-30 Hz in STN-LFP without a preferential directionality of interactions between different contacts along the electrode tracks. While the direction of neuronal interactions per se was preserved after levodopa administration, functional connectivity and the ventral-dorsal information flow were modulated by medication. The OFF-ON differences in functional connectivity were correlated with the levodopa-induced improvement in clinical Unified Parkinson's Disease Rating Scale scores. We hypothesize that regional neuronal interactions, as reflected in STN-LFP connectivity, might represent a basis for the intra-nuclear spatial specificity of deep brain stimulation. Moreover, our results suggest the potential use of volume conduction-insensitive measures of connectivity in STN-LFP as a marker of clinical motor symptoms in PD.

Long-range temporal correlations in the subthalamic nucleus of patients with Parkinson's disease.

Neuronal activity in the subthalamic nucleus (STN) of patients with Parkinson's disease (PD) is characterised by excessive neuronal synchronization, particularly in the beta frequency range. However, less is known about the temporal dynamics of neuronal oscillations in PD. In this respect long-range temporal correlations (LRTC) are of special interest as they quantify the neuronal dynamics on different timescales and have been shown to be relevant for optimal information processing in the brain. While the presence of LRTC has been demonstrated in cortical data, their existence in deep brain structures remains an open question. We investigated (i) whether LRTC are present in local field potentials (LFP) recorded bilaterally from the STN at wakeful rest in ten patients with PD after overnight withdrawal of levodopa (OFF) and (ii) whether LRTC can be modulated by levodopa treatment (ON). Detrended fluctuation analysis was utilised in order to quantify the temporal dynamics in the amplitude fluctuations of LFP oscillations. We demonstrated for the first time the presence of LRTC (extending up to 50 s) in the STN. Importantly, the ON state was characterised by significantly stronger LRTC than the OFF state, both in beta (13-35 Hz) and high-frequency (> 200 Hz) oscillations. The existence of LRTC in subcortical structures such as STN provides further evidence for their ubiquitous nature in the brain. The weaker LRTC in the OFF state might indicate limited information processing in the dopamine-depleted basal ganglia. The present results implicate LRTC as a potential biomarker of pathological neuronal processes in PD.

Towards non-invasive multi-unit spike recordings: mapping 1kHz EEG signals over human somatosensory cortex.

OBJECTIVE: Scalp-derived human somatosensory evoked potentials (SEPs) contain high-frequency oscillations (600 Hz; 'sigma-burst') reflecting concomitant bursts of spike responses in primary somatosensory cortex that repeat regularly at 600 Hz. Notably, recent human intracranial SEP have revealed also 1 kHz responses ('kappa-burst'), possibly reflecting non-rhythmic spiking summed over multiple cells (MUA: multi-unit activity). However, the non-invasive detection of EEG signals at 1 kHz typical for spikes has always been limited by noise contributions from both, amplifier and body/electrode interface. Accordingly, we developed a low-noise recording set-up optimised to map non-invasively 1 kHz SEP components. METHODS: SEP were recorded upon 4 Hz left median nerve stimulation in 6 healthy human subjects. Scalp potentials were acquired inside an electrically and magnetically shielded room using low-noise custom-made amplifiers. Furthermore, in order to reduce thermal Johnson noise contributions from the sensor/skin interface, electrode impedances were adjusted to ≤ 1 kΩ. Responses averaged after repeated presentation of the stimulus (n=4000 trials) were evaluated by spatio-temporal pattern analyses in complementary spectral bands. RESULTS: Three distinct spectral components were identified: N20 (<100 Hz), sigma-burst (450-750 Hz), and kappa-burst (850-1200 Hz). The two high-frequency bursts (sigma, kappa) exhibited distinct and partially independent spatiotemporal evolutions, indicating subcortical as well as several cortical generators. CONCLUSIONS: Using a dedicated low-noise set-up, human SEP 'kappa-bursts' at 1 kHz can be non-invasively detected and their scalp distribution be mapped. Their topographies indicate a set of subcortical/cortical generators, at least partially distinct from the topography of the 600 Hz sigma-bursts described previously. SIGNIFICANCE: The non-invasive detection and surface mapping of 1 kHz EEG signals presented here provides an essential step towards non-invasive monitoring of multi-unit spike activity.

Extension of non-invasive EEG into the kHz range for evoked thalamocortical activity by means of very low noise amplifiers.

Ultrafast electroencephalographic signals, having frequencies above 500 Hz, can be observed in somatosensory evoked potential measurements. Usually, these recordings have a poor signal-to-noise ratio (SNR) because weak signals are overlaid by intrinsic noise of much higher amplitude like that generated by biological sources and the amplifier. As an example, recordings at the scalp taken during electrical stimulation of the median nerve show a 600 Hz burst with submicro-volt amplitudes which can be extracted from noise by the use of massive averaging and digital signal processing only. We have investigated this signal by means of a very low noise amplifier made in-house (minimal voltage noise 2.7 nV Hz(-1/2), FET inputs). We examined how the SNR of the data is altered by the bandwidth and the use of amplifiers with different intrinsic amplifier noise levels of 12 and 4.8 nV Hz(-1/2), respectively. By analyzing different frequency contributions of the signal, we found an extremely weak 1 kHz component superimposed onto the well-known 600 Hz burst. Previously such high-frequency electroencephalogram responses around 1 kHz have only been observed by deep brain electrodes implanted for tremor therapy of Parkinson patients. For the non-invasive measurement of such signals, we recommend that amplifier noise should not exceed 4 nV Hz(-1/2).

Nonlinear interactions of high-frequency oscillations in the human somatosensory system.

OBJECTIVE: The source of somatosensory evoked high-frequency activity at about 600 Hz is still not completely clear. Hence, we aimed to study the influence of double stimulation on the human somatosensory system by analyzing both the low-frequency activity and the high-frequency oscillations (HFOs) at about 600 Hz. METHODS: We used median nerve stimulation at seven interstimuli intervals (ISIs) with a high time resolution between 2.4 and 4.8 ms to investigate the N15, N20 and superimposed HFOs. Simultaneously, the electroencephalogram and the magnetoencephalogram of 12 healthy participants were recorded. Subsequently, the source analysis of precortical and cortical dipoles was performed. RESULTS: The difference computations of precortical dipole activation curves showed in both the low- and high-frequency range a correlation between the ISI and the latency of the second stimulus response. The cortical low-frequency response showed a similar behavior. Contrarily, in the second response of cortical HFOs this latency shift could not be confirmed. We found amplitude fluctuations that were dependent on the ISI in the low-frequency activity and the HFOs. These nonlinear interactions occurred at ISIs, which differ by one full HFO period (1.6 ms). CONCLUSIONS: Low-frequency activity and HFOs originate from different generators. Precortical and cortical HFOs are independently generated. The amplitude fluctuations dependent on ISI indicate nonlinear interference between successive stimuli. SIGNIFICANCE: Information processing in human somatosensory system includes nonlinearity.

Anticipatory activity in the human thalamus is predictive of reaction times.

Responding to environmental stimuli in a fast manner is a fundamental behavioral capacity. The pace at which one responds is known to be predetermined by cortical areas, but it remains to be shown if subcortical structures also take part in defining motor swiftness. As the thalamus has previously been implicated in behavioral control, we tested if neuronal activity at this level could also predict the reaction time of upcoming movements. To this end we simultaneously recorded electrical brain activity from the scalp and the ventral intermediate nucleus (VIM) of the thalamus in patients undergoing thalamic deep brain stimulation. Based on trial-to-trial analysis of a Go/NoGo task, we demonstrate that both cortical and thalamic neuronal activity prior to the delivery of upcoming Go stimulus correlates with the reaction time. This result goes beyond the demonstration of thalamic activity being associated with but potentially staying invariant to motor performance. In contrast, it indicates that the latencies at which we respond to environmental stimuli are not exclusively related to cortical pre-movement states but are also correlated with anticipatory thalamic activity.

Recommendations for the clinical use of somatosensory-evoked potentials.

The International Federation of Clinical Neurophysiology (IFCN) is in the process of updating its Recommendations for clinical practice published in 1999. These new recommendations dedicated to somatosensory-evoked potentials (SEPs) update the methodological aspects and general clinical applications of standard SEPs, and introduce new sections dedicated to the anatomical-functional organization of the somatosensory system and to special clinical applications, such as intraoperative monitoring, recordings in the intensive care unit, pain-related evoked potentials, and trigeminal and pudendal SEPs. Standard SEPs have gained an established role in the health system, and the special clinical applications we describe here are drawing increasing interest. However, to prove clinically useful each of them requires a dedicated knowledge, both technical and pathophysiological. In this article we give technical advice, report normative values, and discuss clinical applications.

Effects of subthalamic deep brain stimulation on dysarthrophonia in Parkinson's disease.

BACKGROUND: Motor deficits in Parkinson's disease (PD) are reduced by deep brain stimulation (DBS) of the subthalamic nucleus (STN), but the impact of this therapy on dysarthrophonic problems in PD remains controversial. We therefore aimed to disentangle the effects of STN DBS on the speech skills of long-term treated patients. METHODS: Under continued medication, speech and motor functions of 19 patients with PD with bilateral STN DBS were studied when their therapeutic stimulation was active (STIM-ON) versus switched off (STIM-OFF). Per condition, perceptual speech ratings were given by: (i) the patients themselves, (ii) the treating physician, and (iii) professional speech therapists. Furthermore, single speech parameters were measured with a battery of technical exams in both STIM-ON and STIM-OFF. RESULTS: STN DBS significantly worsened speech performance according to all perceptual rating methods applied. In contrast, technical measures showed DBS-induced improvements of single speech dimensions affected by the PD-specific motor disorder. These changes occurred independently of the reduction of motor impairment, which was consistently effectuated by STN DBS. CONCLUSION: In parallel to the beneficial effects on the motor symptoms of PD, STN DBS reduces designated disease-inherent dysarthrophonic symptoms, such as glottic tremor. However, these actions on speech are predominantly outweighed by the general dysarthrogenic effects of STN DBS, probably based on a decline of complex (eg, prosodic) functions. Thus, stimulation-induced speech impairment should be considered a likely problem in the course of this treatment.

DC-magnetoencephalography and time-resolved near-infrared spectroscopy combined to study neuronal and vascular brain responses.

The temporal relation between vascular and neuronal responses of the brain to external stimuli is not precisely known. For a better understanding of the neuro-vascular coupling changes in cerebral blood volume and oxygenation have to be measured simultaneously with neuronal currents. With this motivation modulation dc-magnetoencephalography was combined with multi-channel time-resolved near-infrared spectroscopy to simultaneously monitor neuronal and vascular parameters on a scale of seconds. Here, the technique is described, how magnetic and optical signals can be measured simultaneously. In a simple motor activation paradigm (alternating 30 s of finger movement with 30 s of rest for 40 min) both signals were recorded non-invasively over the motor cortex of eight subjects. The off-line averaged signals from both modalities showed distinct stimulation related changes. By plotting changes in oxy- or deoxyhaemoglobin as a function of magnetic field a characteristic trajectory was created, which was similar to a hysteresis loop. A parametric analysis allowed quantitative results regarding the timing of coupling: the vascular signal increased significantly slower than the neuronal signal.

On-line differentiation of neuroelectric activities: algorithms and applications.

This paper discusses machine learning methods and their application to Brain-Computer Interfacing. A particular focus is placed on linear classification methods which can be applied in the BCI context. Finally, we provide an overview of the Berlin-Brain Computer Interface (BBCI).

Different origins of low- and high-frequency components (600 Hz) of human somatosensory evoked potentials.

OBJECTIVE: Human median nerve somatosensory evoked potentials (SEPs) contain a low-amplitude (<500 nV) high-frequency (approximately 600 Hz) burst of repetitive wavelets (HFOs) which are superimposed onto the primary cortical response 'N20.' This study aimed to further clarify the cortical and subcortical structures involved in the generation of the HFOs. METHODS: 128-Channel recordings were obtained to right median nerve stimulation of 10 right-handed healthy human subjects and in 7 of them additional to right ulnar nerve. Data were evaluated by applying principal component analysis and dipole source analysis. RESULTS: Different source evaluation strategies provided converging evidence for a cortical HFO origin, with two different almost orthogonally oriented generators being active in parallel, but with a phase shift of a quarter of their oscillatory period, while the low-frequency 'N20' is adequately modeled by one tangential dipole source. Median and ulnar derived low-frequency and HFO cortical sources show a somatotopic order. Additionally, generation of the HFOs was localized in subcortical, near-thalamic and subthalamic source sites. The near-thalamic dipole was located at significantly different sites in HFO and low-frequency data. CONCLUSIONS: The cortical HFO source constellation points to a 'precortical' source in terminals of thalamocortical fibers and a second intracortical HFO origin. Furthermore, HFOs are also generated at subcortical and even subthalamic sites. Near-thalamic, the HFO and low-frequency signals are generated or modulated by different neuron populations involved in the thalamocortical outflow.

Improving speed and accuracy of brain-computer interfaces using readiness potential features.

To enhance human interaction with machines, research interest is growing to develop a 'brain-computer interface', which allows communication of a human with a machine only by use of brain signals. So far, the applicability of such an interface is strongly limited by low bit-transfer rates, slow response times and long training sessions for the subject. The Berlin Brain-Computer Interface (BBCI) project is guided by the idea to train a computer by advanced machine learning techniques both to improve classification performance and to reduce the need of subject training. In this paper we present two directions in which brain-computer interfacing can be enhanced by exploiting the lateralized readiness potential: (1) for establishing a rapid response BCI system that can predict the laterality of upcoming finger movements before EMG onset even in time critical contexts, and (2) to improve information transfer rates in the common BCI approach relying on imagined limb movements.