Yin Fen Low

Bio: Yin Fen Low is an academic researcher from Universiti Teknikal Malaysia Melaka. The author has contributed to research in topics: Tinnitus & Transcranial magnetic stimulation. The author has an hindex of 13, co-authored 32 publications receiving 401 citations. Previous affiliations of Yin Fen Low include Saarland University.

Objective Quantification of the Tinnitus Decompensation by Synchronization Measures of Auditory Evoked Single Sweeps

Abstract: Large-scale neural correlates of the tinnitus decompensation might be used for an objective evaluation of therapies and neurofeedback based therapeutic approaches. In this study, we try to identify large-scale neural correlates of the tinnitus decompensation using wavelet phase stability criteria of single sweep sequences of late auditory evoked potentials as synchronization stability measure. The extracted measure provided an objective quantification of the tinnitus decompensation and allowed for a reliable discrimination between a group of compensated and decompensated tinnitus patients. We provide an interpretation for our results by a neural model of top-down projections based on the Jastreboff tinnitus model combined with the adaptive resonance theory which has not been applied to model tinnitus so far. Using this model, our stability measure of evoked potentials can be linked to the focus of attention on the tinnitus signal. It is concluded that the wavelet phase stability of late auditory evoked potential single sweeps might be used as objective tinnitus decompensation measure and can be interpreted in the framework of the Jastreboff tinnitus model and adaptive resonance theory.

Alterations in Event Related Potentials (ERP) associated with tinnitus distress and attention.

Abstract: Tinnitus related distress corresponds to different degrees of attention paid to the tinnitus. Shifting attention to a signal other than the tinnitus is therefore particularly difficult for patients with high tinnitus related distress. As attention effects on Event Related Potentials (ERP) have been shown this should be reflected in ERP measurements (N100, phase locking). In order to prove this hypothesis single sweep ERP recordings were obtained in 41 tinnitus patients as well as 10 control subjects during a period of time when attention was shifted to a tone (attended) and during a second phase (unattended) when they did not focus attention to the tone. Whereas tinnitus patients with low distress showed a significant reduction in both N100 amplitude and phase locking when comparing the attended and unattended measurement condition a group of patients with high tinnitus related distress did not show such ERP alterations. Using single sweep ERP measurements the results of our study show, that attention in high tinnitus related distress patients is captured by their tinnitus significantly more than in low distress patients. Furthermore our results provide the basis for future neurofeedback based tinnitus therapies aiming at maximizing the ability to shift attention away from the tinnitus.

Feature extraction of electroencephalogram (EEG) signal - A review

Abstract: This paper presents a review on signal analysis method for feature extraction of electroencephalogram (EEG) signal. It is an important aspect in signal processing as the result obtained will be used for signal classification. A good technique for feature extraction is necessary in order to achieve robust classification of signal. Considering several techniques have been implemented for extracting features in EEG signal, we only highlight the most commonly used for schizophrenia. The techniques are Hilbert-Huang transform, Principal Component Analysis, Independent Component Analysis and Local Discriminant Bases. Despite of their drawbacks, they can be applied which depends on the aim of a research, parameters and the data collected. Nevertheless, these techniques can be modified so that the new algorithm can overcome the shortcomings of original algorithm or algorithm beforehand. The modified Local Discriminant Bases algorithm is introduced in the present paper as another powerful adaptive feature extraction technique for EEG signal which is not reported elsewhere in investigating schizophrenia.

Extraction of Auditory Attention Correlates in Single Sweeps of Cortical Potentials by Maximum Entropy Paradigms and its Application

Abstract: Recently, we have shown that the wavelet phase synchronization stability of single sweeps of auditory late responses (ALRs) allows for the quantification of the tinnitus decompensation. Our underlying model of adaptive resonance and spotlighting of attention links the synchronization stability directly to neural correlates of attention reflected in ALRs. Correlates of this attentional mechanism are further investigated in this study by using an auditory paradigm based on maximum entropy principle in healthy subjects. In particular, we show that the wavelet phase synchronization of ALR single sweeps allows for a direct online monitoring of phase locked auditory attention. Such an online monitoring cannot be implemented by known procedures as they are based on large-scale averages of ALRs. Apart from the objective quantification of the tinnitus decompensation, this measure can be used in every online and real time neurofeedback therapeutic approach where a direct stimulus locked attention monitoring is mandatory

Partial directed coherence: a new concept in neural structure determination

Abstract: Abstract. This paper introduces a new frequency-domain approach to describe the relationships (direction of information flow) between multivariate time series based on the decomposition of multivariate partial coherences computed from multivariate autoregressive models. We discuss its application and compare its performance to other approaches to the problem of determining neural structure relations from the simultaneous measurement of neural electrophysiological signals. The new concept is shown to reflect a frequency-domain representation of the concept of Granger causality.

Classification of EEG Signals Based on Autoregressive Model and Wavelet Packet Decomposition

Abstract: Classification of electroencephalogram (EEG) signals is an important task in the brain computer interface system. This paper presents two combination strategies of feature extraction on EEG signals. In the first strategy, Autoregressive coefficients and approximate entropy are calculated respectively, and the features are obtained by assembling them. In the second strategy, the EEG signals are first decomposed into sub-bands by wavelet packet decomposition. Wavelet packet coefficients are then sent to the autoregressive model to calculate autoregressive coefficients, which are used as features extracted from the original EEG signals. These features are fed to support vector machine for classifying the EEG signals. The classification accuracy has been used for evaluating the classification performance. Experimental results in five mental tasks show that the combination strategies can effectively improve the classification performance when the order of autoregressive model is greater than 5, and the second strategy is superior to the first one in terms of the classification accuracy.

State-dependencies of learning across brain scales

Abstract: Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous "spontaneous" shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly.