Low-frequency local field potentials in primate motor cortex and their application to neural interfaces

PhD ThesisFor patients with spinal cord injury and paralysis, there are currently very limited options for clinical therapy. Brain-machine interfaces (BMIs) are neuroprosthetic devices that are being developed to record from the motor cortex in such patients, bypass the spinal lesion, and use decoded signals to control an effector, such as a prosthetic limb. The ideal BMI would be durable, reliable, totally predictable, fully-implantable, and have generous battery life. Current, state-of-the-art BMIs are limited in all of these domains; partly because the typical signals used—neuronal action potentials, or ‘spikes’—are very susceptible to micro-movement of recording electrodes. Recording spikes from the same neurons over many months is therefore difficult, and decoder behaviour may be unpredictable from day-today. Spikes also need to be digitized at high frequencies (~104 Hz) and heavily processed. As a result, devices are energy-hungry and difficult to miniaturise. Low-frequency local field potentials (lf-LFPs; < 5 Hz) are an alternative cortical signal. They are more stable and can be captured and processed at much lower frequencies (~101 Hz). Here we investigate rhythmical lf-LFP activity, related to the firing of local cortical neurons, during isometric wrist movements in Rhesus macaques. Multichannel spike-related slow potentials (SRSPs) can be used to accurately decode the firing rates of individual motor cortical neurons, and subjects can control a BMI task using this synthetic signal, as if they were controlling the actual firing rate. Lf-LFP–based firing rate estimates are stable over time – even once actual spike recordings have been lost. Furthermore, the dynamics of lf-LFPs are distinctive enough, that an unsupervised approach can be used to train a decoder to extract movement-related features for use in biofeedback BMIs. Novel electrode designs may help us optimise the recording of these signals, and facilitate progress towards a new generation of robust, implantable BMIs for patients.Research Studentship from the MRC, and Andy Jackson’s laboratory (hence this work) is supported by the Wellcome Trust

Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis

Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real EEG data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple linear transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from ten healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs

Rehabilitation of hand in subacute tetraplegic patients based on brain computer interface and functional electrical stimulation: a randomised pilot study

Objective. To compare neurological and functional outcomes between two groups of hospitalised patients with subacute tetraplegia. Approach. Seven patients received 20 sessions of brain computer interface (BCI) controlled functional electrical stimulation (FES) while five patients received the same number of sessions of passive FES for both hands. The neurological assessment measures were event related desynchronization (ERD) during movement attempt, Somatosensory evoked potential (SSEP) of the ulnar and median nerve; assessment of hand function involved the range of motion (ROM) of wrist and manual muscle test. Main results. Patients in both groups initially had intense ERD during movement attempt that was not restricted to the sensory-motor cortex. Following the treatment, ERD cortical activity restored towards the activity in able-bodied people in BCI-FES group only, remaining wide-spread in FES group. Likewise, SSEP returned in 3 patients in BCI-FES group, having no changes in FES group. The ROM of the wrist improved in both groups. Muscle strength significantly improved for both hands in BCI-FES group. For FES group, a significant improvement was noticed for right hand flexor muscles only. Significance. Combined BCI-FES therapy results in better neurological recovery and better improvement of muscle strength than FES alone. For spinal cord injured patients, BCI-FES should be considered as a therapeutic tool rather than solely a long-term assistive device for the restoration of a lost function

Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification

Objective. The main goal of this work is to develop a model for multi-sensor signals such as MEG or EEG signals, that accounts for the inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI type experiments. Approach. The method involves linear mixed effects statistical model, wavelet transform and spatial filtering, and aims at the characterization of localized discriminant features in multi-sensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e. discriminant) and background noise, using a very simple Gaussian linear mixed model. Main results. Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data, in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context. Significance. The combination of linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves on earlier results on similar problems, and the three main ingredients all play an important role

Parametric Modelling of EEG Data for the Identification of Mental Tasks

Electroencephalographic (EEG) data is widely used as a biosignal for the identification of different mental states in the human brain. EEG signals can be captured by relatively inexpensive equipment and acquisition procedures are non-invasive and not overly complicated. On the negative side, EEG signals are characterized by low signal-to-noise ratio and non-stationary characteristics, which makes the processing of such signals for the extraction of useful information a challenging task.peer-reviewe

Time course and robustness of ERP object and face differences

Conflicting results have been reported about the earliest “true” ERP differences related to face processing, with the bulk of the literature focusing on the signal in the first 200 ms after stimulus onset. Part of the discrepancy might be explained by uncontrolled low-level differences between images used to assess the timing of face processing. In the present experiment, we used a set of faces, houses, and noise textures with identical amplitude spectra to equate energy in each spatial frequency band. The timing of face processing was evaluated using face–house and face–noise contrasts, as well as upright-inverted stimulus contrasts. ERP differences were evaluated systematically at all electrodes, across subjects, and in each subject individually, using trimmed means and bootstrap tests. Different strategies were employed to assess the robustness of ERP differential activities in individual subjects and group comparisons. We report results showing that the most conspicuous and reliable effects were systematically observed in the N170 latency range, starting at about 130–150 ms after stimulus onset

A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities

Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits. In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions. Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results. EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications

Induced gamma-band activity is related to the time point of object identification

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