Robust moving averages, with Hopfield neural network implementation, for monitoring evoked potential signals

A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses

The detection of mild cognitive impairment (MCI), the transitional stage between normal cognitive changes of aging and the cognitive decline caused by AD, is of paramount clinical importance, since MCI patients are at increased risk of progressing into AD. Electroencephalographic (EEG) alterations in the spectral content of brainwaves and connectivity at resting state have been associated with early-stage AD. Recently, cognitive event-related potentials (ERPs) have entered into the picture as an easy to perform screening test. Motivated by the recent findings about the role of cross-frequency coupling (CFC) in cognition, we introduce a relevant methodological approach for detecting MCI based on cognitive responses from a standard auditory oddball paradigm. By using the single trial signals recorded at Pz sensor and comparing the responses to target and non-target stimuli, we first demonstrate that increased CFC is associated with the cognitive task. Then, considering the dynamic character of CFC, we identify instances during which the coupling between particular pairs of brainwave frequencies carries sufficient information for discriminating between normal subjects and patients with MCI. In this way, we form a multiparametric signature of impaired cognition. The new composite biomarker was tested using data from a cohort that consists of 25 amnestic MCI patients and 15 age-matched controls. Standard machine-learning algorithms were employed so as to implement the binary classification task. Based on leave-one-out cross-validation, the measured classification rate was found reaching very high levels (95%). Our approach compares favorably with the traditional alternative of using the morphology of averaged ERP response to make the diagnosis and the usage of features from spectro-temporal analysis of single-trial responses. This further indicates that task-related CFC measurements can provide invaluable analytics in AD diagnosis and prognosis.

Cortical activity preceding vertical saccades: a MEG study

Previous studies have shown that upward saccade latencies are faster than downward saccade latencies in certain tasks. This asymmetry does not appear to represent a general main effect of the visual, or the vertical oculomotor system. In this study we examined the cortical activity underlying this latency asymmetry. We used MEG to assess cortical activity related to horizontal and vertical saccade preparation, and eye movement recordings to assess saccade latencies in a modified delay task. The reconstructed cortical activity was examined with respect to the onset of the target stimulus and the onset of the saccade. Upward saccades were faster than downward saccades, in agreement with previous studies. Although to a large extent, horizontal and vertical targets activated similar areas, there were also some differences. The earlier difference was found 100-150 ms after target onset over the right supramarginal gyrus when subjects attended to location-cues. Down cues activated this area faster than up cues. Moreover, cue-related activity was stronger over the left frontal cortex for up than down cues. In contrast, saccade-related activity over the same area was stronger when preceding downward than upward saccades. The results suggest that stimuli in the upper and lower visual field may have different impacts on accessing networks related to visual attention and motor preparation resulting in different behavioral asymmetries.

Magnetoencephalography (MEG)

Magnetoencephalography (MEG) encompasses a family of non-contact, non-invasive techniques for detecting the magnetic field generated by the electrical activity of the brain, for analyzing this MEG signal and for using the results to study brain function. The overall purpose of MEG is to extract estimates of the spatiotemporal patterns of electrical activity in the brain from the measured magnetic field outside the head. The electrical activity in the brain is a manifestation of collective neuronal activity and, to a large extent, the currency of brain function. The estimates of brain activity derived from MEG can therefore be used to study mechanisms and processes that support normal brain function in humans and help us understand why, when and how they fail.

Fuzzy logic control for intracranial pressure via continuous propofol sedation in a neurosurgical intensive care unit

The major goal of this paper is to provide automatically continuous propofol sedation for patients with severe head injury, unconsciousness, and mechanical ventilation in order to reduce the effect of agitation on intracranial pressure (ICP) using fuzzy logic control in a neurosurgical intensive care unit (NICU). Seventeen patients were divided into three groups in which control was provided with three different controllers. Experimental control periods were of 60min duration in all cases. Group A used a conventional rule-based controller (RBC), Group B a fuzzy logic controller (FLC), and Group C a self-organizing fuzzy logic controller (SOFLC). The performance of the controllers was analyzed by ICP pattern of sedation. The ICP pattern of errors was analyzed for mean and root mean square deviation (RMSD) for the entire duration of control (i.e., 1h). The results indicate that FLC can easily mimic the rule-base of human experts (i.e., neurosurgeons) to achieve stable sedation similar to the RBC group. Furthermore, the results also show that a SOFLC can provide more stable sedation of ICP pattern because it can modify the fuzzy rule-base to compensate for inter-patient variations.

The human primary somatosensory cortex response contains components related to stimulus frequency and perception in a frequency discrimination task

Somatosensory stimulation of primary somatosensory cortex (SI) using frequency discrimination offers a direct, well-defined and accessible way of studying cortical decisions at the locus of early input processing. Animal studies have identified and classified the neuronal responses in SI but they have not yet resolved whether during prolonged stimulation the collective SI response just passively reflects the input or actively participates in the comparison and decision processes. This question was investigated using tomographic analysis of single trial magnetoencephalographic data. Four right-handed males participated in a frequency discrimination task to detect changes in the frequency of an electrical stimulus applied to the right-hand digits 2+3+4. The subjects received approximately 600 pairs of stimuli with Stim1 always at 21 Hz, while Stim2 was either 21 Hz (50%) or varied from 22 to 29 Hz in steps of 1 Hz. Both stimuli were 1 s duration, separated by a 1 s interval of no stimulation. The left-SI was the most consistently activated area and showed the first activation peak at 35-48 ms after Stim1 onset and sustained activity during both stimulus periods. During the Stim2 period, we found that the left-SI activation started to differ significantly between two groups of trials (21 versus 26-29 Hz) within the first 100 ms and this difference was sustained and enhanced thereafter (approximately 600 ms). When only correct responses from the above two groups were used, the difference was even higher at later latencies (approximately 650 ms). For one subject who had enough trials of same perception to different input frequencies, e.g. responded 21 Hz to Stim2 at 21 Hz (correct) and 26-29 Hz (error), we found the sustained difference only before 650 ms. Our results suggest that SI is involved with the analysis of an input frequency and related to perception and decision at different latencies.

Semantic geodesic maps: a unifying geometrical approach for studying the structure and dynamics of single trial evoked responses

OBJECTIVES

A general framework for identifying and describing structure in a given sample of evoked response single-trial signals (STs) is introduced. The approach is based on conceptually simple geometrical ideas and enables the convergence of pattern analysis and non-linear time series analysis.

METHODS

Classical steps for analyzing the STs by waveform are first employed and the ST-analysis is transferred to a multidimensional space, the feature space, the geometry of which is systematically studied via multidimensional scaling (MDS) techniques giving rise to semantic maps. The structure in the feature space characterizes the trial-to-trial variability and this is utilized to probe functional connectivity between two brain areas. The underlying dynamic process responsible for the emerged structure can be described by a multidimensional trajectory in the feature space. This in turn enables the detection of dynamical interareal coupling as similarity between the corresponding trajectories.

RESULTS AND CONCLUSIONS

The utility of semantic maps was demonstrated using magnetoencephalographic data from a simple auditory paradigm. The coupling of ongoing activity and evoked response is vividly demonstrated and contrasted with the apparent deflection from zero baseline that survives averaging. Prototypes are easily identified as the end points of distinct paths in the semantic map representation, and their neighborhood is populated by STs with distinct properties not only in the latencies where the evoked response is expected to be strong, but also and very significantly in the prestimulus period. Finally our results provide evidence for interhemispheric binding in the (4-8 Hz) range and dynamical coupling at faster time scales.

Electroencephalogram processing using neural networks

The electroencephalogram (EEG), a highly complex signal, is one of the most common sources of information used to study brain function and neurological disorders. More than 100 current neural network applications dedicated to EEG processing are presented. Works are categorized according to their objective (sleep analysis, monitoring anesthesia depth, brain-computer interface, EEG artifact detection, EEG source-based localization, etc.). Each application involves a specific approach (long-term analysis or short-term EEG segment analysis, real-time or time delayed processing, single or multiple EEG-channel analysis, etc.), for which neural networks were generally successful. The promising performances observed are demonstrative of the efficiency and efficacy of systems developed. This review can aid researchers, clinicians and implementors to understand up-to-date interest in neural network tools for EEG processing. The extended bibliography provides a database to assist in possible new concepts and idea development.

Timing and connectivity in the human somatosensory cortex from single trial mass electrical activity

Parallel-distributed processing is ubiquitous in the brain but often ignored by experimental designs and methods of analysis, which presuppose sequential and stereotypical brain activations. We introduce here a methodology that can effectively deal with sequential and distributed activity. Regional brain activations elicited by electrical median nerve stimulation are identified in tomographic estimates extracted from single trial magnetoencephalographic signals. Habituation is identified in both primary somatosensory cortex (SI) and secondary somatosensory cortex (SII), often interrupted by resurgence of strong activations. Pattern analysis is used to identify single trials with homogeneous regional brain activations. Common activity patterns with well-defined connectivity are identified within each homogeneous group of single trials across the subjects studied. On the contralateral side one encounters distinct sets of single trials following identical stimuli. We observe in one set of trials sequential activation from SI to SII and insula with onset of SII at 60 msec, whereas in the other set simultaneous early co-activations of the same two areas.

Exploratory data analysis of evoked response single trials based on minimal spanning tree

OBJECTIVE

An exploratory data analysis framework, based on minimal spanning tree, is proposed as a means to support the analysis of single trial (ST) electrophysiological signals. The core of this framework is the compact description of the input ST sample in a form of content-dependent ordered lists. Based on the established hierarchies, efficient ways to increase the SNR, extract prototypical responses, visualize possible self-organization trends in the sample and track the course of evoked response along the trial-to-trial dimension, are proposed.

METHOD

Magnetoencephalographic auditory evoked responses were used for demonstrating and validating the introduced framework.

RESULTS AND CONCLUSION

The results demonstrate the benefits, from this intelligent manipulation of STs, in understanding and enhancing the actual evoked signal. Specifically we find support for stimulus-induced phase-resetting hypothesis in the 3-20 Hz band, the existence of trials void of the prototypical evoked response, and an order across the single trial set hinting at an underlying process with long time scale.

Identification of hemifield single trial PVEP on the basis of generalized dynamic neural network classifiers

This paper is concerned with the application of generalized dynamic neural networks for the identification of hemifield pattern-reversal visual evoked potentials. The identification process is performed by different networks with time-varying weights using signals from different electrode positions as external inputs. Since dynamic neural networks are able to process time-varying signals, the identification of the stimulated hemiretinae is performed without feature extraction. The performance of the method presented is compared with a reference method based on the values of instantaneous frequency at the occipital electrode positions at P100 latency.

Identification of the stimulated hemiretinae using a reduced number of PVEP trials

Left and right hemifield pattern-reversal visual evoked potentials (PVEP) at P100 latency are characterised by typical field distributions of potential and the spectral parameter instantaneous frequency (IF). Both parameters can be utilised for the correct identification of the stimulated hemiretina in healthy volunteers (Hoffmann, K. et al. Electroenceph. clin. Neurophysiol., 100, 1996: 569-578). The aim of this study was the investigation of the robustness of instantaneous frequency for reduced numbers of averages. Hemifield PVEP of 15 volunteers (20 channel records) were analysed. The number of averages was reduced step-by-step (64-32-16-8-4-1). For each average, the time of P100 latency was determined by the Global field power maximum between 90 and 125 ms. The stimulated hemifield was identified using the potential or instantaneous frequency values at the occipital electrode positions O1 and O2: by maximal potential and instantaneous frequency on the stimulus-contralateral side. In summary, by reduced numbers of averages (as well as by single trials) the stimulated hemiretina was correctly identified more frequently on the basis of instantaneous frequency than of potential distribution. A number of 8 averages seems to be sufficient for the correct identification of the stimulus condition. Consequently, for the identification of left and right hemifield stimulations the recording time could be reduced immensely. Instantaneous frequency is suggested as an additional and robust parameter for the selective averaging of artefact-free trials during the recording of hemifield PVEP.