Identification of audio evoked response potentials in ambulatory EEG data

Electroencephalography (EEG) is commonly used for observing brain function over a period of time. It employs a set of invasive electrodes on the scalp to measure the electrical activity of the brain. EEG is mainly used by researchers and clinicians to study the brain’s responses to a specific stimulus - the event-related potentials (ERPs). Different types of undesirable signals, which are known as artefacts, contaminate the EEG signal. EEG and ERP signals are very small (in the order of microvolts); they are often obscured by artefacts with much larger amplitudes in the order of millivolts. This greatly increases the difficulty of interpreting EEG and ERP signals.Typically, ERPs are observed by averaging EEG measurements made with many repetitions of the stimulus. The average may require many tens of repetitions before the ERP signal can be observed with any confidence. This greatly limits the study and useof ERPs. This project explores more sophisticated methods of ERP estimation from measured EEGs. An Optimal Weighted Mean (OWM) method is developed that forms a weighted average to maximise the signal to noise ratio in the mean. This is developedfurther into a Bayesian Optimal Combining (BOC) method where the information in repetitions of ERP measures is combined to provide a sequence of ERP estimations with monotonically decreasing uncertainty. A Principal Component Analysis (PCA) isperformed to identify the basis of signals that explains the greatest amount of ERP variation. Projecting measured EEG signals onto this basis greatly reduces the noise in measured ERPs. The PCA filtering can be followed by OWM or BOC. Finally, crosschannel information can be used. The ERP signal is measured on many electrodes simultaneously and an improved estimate can be formed by combining electrode measurements. A MAP estimate, phrased in terms of Kalman Filtering, is developed using all electrode measurements.The methods developed in this project have been evaluated using both synthetic and measured EEG data. A synthetic, multi-channel ERP simulator has been developed specifically for this project.Numerical experiments on synthetic ERP data showed that Bayesian Optimal Combining of trial data filtered using a combination of PCA projection and Kalman Filtering, yielded the best estimates of the underlying ERP signal. This method has been applied to subsets of real Ambulatory Electroencephalography (AEEG) data, recorded while participants performed a range of activities in different environments. From this analysis, the number of trials that need to be collected to observe the P300 amplitude and delay has been calculated for a range of scenarios

ERP source tracking and localization from single trial EEG MEG signals

Electroencephalography (EEG) and magnetoencephalography (MEG), which are two of a number of neuroimaging techniques, are scalp recordings of the electrical activity of the brain. EEG and MEG (E/MEG) have excellent temporal resolution, they are easy to acquire, and have a wide range of applications in science, medicine and engineering. These valuable signals, however, suffer from poor spatial resolution and in many cases from very low signal to noise ratios. In this study, new computational methods for analyzing and improving the quality of E/MEG signals are presented. We mainly focus on single trial event-related potential (ERP) estimation and E/MEG dipole source localization. Several methods basically based on particle filtering (PF) are proposed. First, a method using PF for single trial estimation of ERP signals is considered. In this method, the wavelet coefficients of each ERP are assumed to be a Markovian process and do not change extensively across trials. The wavelet coefficients are then estimated recursively using PF. The results both for simulations and real data are compared with those of the well known Kalman Filtering (KF) approach. In the next method we move from single trial estimation to source localization of E/MEG signals. The beamforming (BF) approach for dipole source localization is generalized based on prior information about the noise. BF is in fact a spatial filter that minimizes the power of all the signals at the output of the filter except those that come from the locations of interest. In the proposed method, using two more constraints than in the classical BF formulation, the output noise powers are minimized and the interference activities are stopped.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Estimation of single trial ERPs and EEG phase synchronization with application to mental fatigue

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Modeling and Analysis of Neural Spike Trains

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ERP source tracking and localization from single trial EEG MEG signals

Electroencephalography (EEG) and magnetoencephalography (MEG), which are two of a number of neuroimaging techniques, are scalp recordings of the electrical activity of the brain. EEG and MEG (E/MEG) have excellent temporal resolution, they are easy to acquire, and have a wide range of applications in science, medicine and engineering. These valuable signals, however, suffer from poor spatial resolution and in many cases from very low signal to noise ratios. In this study, new computational methods for analyzing and improving the quality of E/MEG signals are presented. We mainly focus on single trial event-related potential (ERP) estimation and E/MEG dipole source localization. Several methods basically based on particle filtering (PF) are proposed. First, a method using PF for single trial estimation of ERP signals is considered. In this method, the wavelet coefficients of each ERP are assumed to be a Markovian process and do not change extensively across trials. The wavelet coefficients are then estimated recursively using PF. The results both for simulations and real data are compared with those of the well known Kalman Filtering (KF) approach. In the next method we move from single trial estimation to source localization of E/MEG signals. The beamforming (BF) approach for dipole source localization is generalized based on prior information about the noise. BF is in fact a spatial filter that minimizes the power of all the signals at the output of the filter except those that come from the locations of interest. In the proposed method, using two more constraints than in the classical BF formulation, the output noise powers are minimized and the interference activities are stopped

Doctor of Philosophy

dissertationThis dissertation describes the use of cortical surface potentials, recorded with dense grids of microelectrodes, for brain-computer interfaces (BCIs). The work presented herein is an in-depth treatment of a broad and interdisciplinary topic, covering issues from electronics to electrodes, signals, and applications. Within the scope of this dissertation are several significant contributions. First, this work was the first to demonstrate that speech and arm movements could be decoded from surface local field potentials (LFPs) recorded in human subjects. Using surface LFPs recorded over face-motor cortex and Wernickes area, 150 trials comprising vocalized articulations of ten different words were classified on a trial-by-trial basis with 86% accuracy. Surface LFPs recorded over the hand and arm area of motor cortex were used to decode continuous hand movements, with correlation of 0.54 between the actual and predicted position over 70 seconds of movement. Second, this work is the first to make a detailed comparison of cortical field potentials recorded intracortically with microelectrodes and at the cortical surface with both micro- and macroelectrodes. Whereas coherence in macroelectrocorticography (ECoG) decayed to half its maximum at 5.1 mm separation in high frequencies, spatial constants of micro-ECoG signals were 530-700 ?m-much closer to the 110-160 ?m calculated for intracortical field potentials than to the macro-ECoG. These findings confirm that cortical surface potentials contain millimeter-scale dynamics. Moreover, these fine spatiotemporal features were important for the performance of speech and arm movement decoding. In addition to contributions in the areas of signals and applications, this dissertation includes a full characterization of the microelectrodes as well as collaborative work in which a custom, low-power microcontroller, with features optimized for biomedical implants, was taped out, fabricated in 65 nm CMOS technology, and tested. A new instruction was implemented in this microcontroller which reduced energy consumption when moving large amounts of data into memory by as much as 44%. This dissertation represents a comprehensive investigation of surface LFPs as an interfacing medium between man and machine. The nature of this work, in both the breadth of topics and depth of interdisciplinary effort, demonstrates an important and developing branch of engineering