In this study, we are interested in brain-machine interfaces that extract movement related cortical potentials (MRCP) from an electroencephalogram (EEG) recorded while driving and use this information to classify/detect when a driver intends to do left or right lane change. Collecting EEGs while driving, however, is a challenging task as it introduces numerous artifacts, including head movements (e.g., to look at side mirrors, etc), eye movements, and whole-body movements. Such artifacts can dominate the signal, thus hampering MRCP and lane change intent detection. Here, we explore three EEG artifact removal algorithms tailored for MRCP detection while driving, namely: headset accelerometer-based independent component analysis (acc-ICA), constrained ICA (cICA) and empirical mode decomposition (EMD). Next, we propose to use a recurrent neural network (RNN) classifier for lane change intent detection and compare results with a conventional support vector machine (SVM) based classifiers. Lastly, we explore the effect of EEG window analysis size on artifact removal and classification performance, thus gauging how close to real-time enhancement can be achieved and with what delay can lane change intent be detected. When looking at averaged trial performance, all system combinations achieved reliable accuracy. On the other hand, for single-trial accuracy, only acc-ICA and EMD methods performed well. Overall, average classification accuracy of 54.25% was achieved with SVMs, whereas accuracies of 82.88%, 82.76% and 82.69% were achieved with the RNN using acc-ICA, cICA and EMD artifact removal algorithms, respectively. Window sizes of 4 seconds prior to lane changes achieved the best result and only six EEG electrodes were required.

Many natural signals, such as voiced speech and some musical instruments, are approximately periodic over short intervals. These signals are often described in mathematics by the sum of sinusoids (harmonics) with frequencies that are proportional to the fundamental frequency, or pitch. In sensor (microphone) array signal processing, the periodic signals are estimated from spatio-temporal samples regarding to the direction of arrival (DOA) of the signal of interest. In this paper, we consider the problem of pitch and DOA estimation of quasi-periodic audio signals. In real-life scenarios, recorded signals are often contaminated by different types of noise, which challenges the assumption of white Gaussian noise in most state-of-the-art methods. We establish filtering methods based on noise statistics to apply to nonparametric spectral and spatial parameter estimates of the harmonics. We design minimum variance solutions with distortionless constraints to estimate the pitch from the frequency estimates, and to estimate the DOA from multichannel phase estimates of the harmonics. Applying this filtering method as the sum of weighted frequency and DOA estimates of the harmonics, we also design a joint DOA and pitch estimator. In white Gaussian noise, we derive even more computationally efficient solutions which are designed using the narrowband power spectrum of the harmonics. Numerical results reveal the performance of the estimators in colored noise compared with the Cramér-Rao lower bound. Experiments on real-life signals indicate the applicability of the methods in practical low local signal-to-noise ratios.

In this paper, we use unconstrained frequency estimates (UFEs) from a noisy harmonic signal and propose two methods to estimate and track the pitch over time. We assume that the UFEs are multivariate-normally-distributed random variables, and derive a maximum likelihood (ML) pitch estimator by maximizing the likelihood of the UFEs over short time-intervals. As the main contribution of this paper, we propose two state-space representations to model the pitch continuity, and, accordingly, we propose two Bayesian methods, namely a hidden Markov model and a Kalman filter. These methods are designed to optimally use the correlations in the consecutive pitch values, where the past pitch estimates are used to recursively update the prior distribution for the pitch variable. We perform experiments using synthetic data as well as a noisy speech recording, and show that the Bayesian methods provide more accurate estimates than the corresponding ML methods.

In signal processing applications of harmonic-structured signals, estimates of the fundamental frequency and number of harmonics are often necessary. In real scenarios, a desired signal is contaminated by different levels of noise and interferers, which complicate the estimation of the signal parameters. In this paper, we present an estimation procedure for harmonic-structured signals in situations with strong interference using spatial filtering, or beamforming. We jointly estimate the fundamental frequency and the constrained model order through the output of the beamformers. Besides that, we extend this procedure to account for inharmonicity using unconstrained model order estimation. The simulations show that beamforming improves the performance of the joint estimates of fundamental frequency and the number of harmonics in low signal to interference (SIR) levels, and an experiment on a trumpet signal show the applicability on real signals.