Alzheimer’s disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.

A modulation spectral representation is investigated for non-intrusive quality and intelligibility measurement of reverberant and dereverberated speech. The representation is obtained by means of an auditory-inspired filterbank analysis of critical-band temporal envelopes of the speech signal. Modulation spectral insights are used to develop an adaptive measure termed speech to reverberation modulation energy ratio. Experimental results show the proposed measure outperforming three standard algorithms for tasks involving estimation of multiple dimensions of perceived coloration, as well as quality measurement and intelligibility estimation of reverberant and dereverberated speech.

This article presents an overview of 12 existing objective speech quality and intelligibility prediction tools. Two classes of algorithms are presented?intrusive and nonintrusive?with the former requiring the use of a reference signal, while the latter does not. Investigated metrics include both those developed for normal hearing (NH) listeners, as well as those tailored particularly for hearing impaired (HI) listeners who are users of assistive listening devices [i.e., hearing aids (HAs) and cochlear implants (CIs)]. Representative examples of those optimized for HI listeners include the speech-to-reverberation modulation energy ratio (SRMR), tailored to HAs (SRMR-HA) and to CIs (SRMR-CI); the modulation spectrum area (ModA); the HA speech quality (HASQI) and perception indices (HASPI); and the perception-model-based quality prediction method for hearing impairments (PEMO-Q-HI). The objective metrics are tested on three subjectively rated speech data sets covering reverberation-alone, noise-alone, and reverberation-plus-noise degradation conditions, as well as degradations resultant from nonlinear frequency compression and different speech enhancement strategies. The advantages and limitations of each measure are highlighted and recommendations are given for suggested uses of the different tools under specific environmental and processing conditions.

Near-infrared spectroscopy (NIRS) has recently been investigated as a non-invasive brain-computer interface (BCI). In particular, previous research has shown that NIRS signals recorded from the motor cortex during left- and right-hand imagery can be distinguished, providing a basis for a two-choice NIRS-BCI. In this study, we investigated the feasibility of an alternative two-choice NIRS-BCI paradigm based on the classification of prefrontal activity due to two cognitive tasks, specifically mental arithmetic and music imagery. Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations (International 10-20 System) while ten able-bodied adults performed mental arithmetic and music imagery within a synchronous shape-matching paradigm. With the 18 filtered AC signals, we created task- and subject-specific maximum likelihood classifiers using hidden Markov models. Mental arithmetic and music imagery were classified with an average accuracy of 77.2% +/- 7.0 across participants, with all participants significantly exceeding chance accuracies. The results suggest the potential of a two-choice NIRS-BCI based on cognitive rather than motor tasks.

This article presents a tutorial overview of models for estimating the quality experienced by users of speech transmission and communication services. Such models can be classified as either parametric or signal based. Signal-based models use input speech signals measured at the electrical or acoustic interfaces of the transmission channel. Parametric models, on the other hand, depend on signal and system parameters estimated during network planning or at run time. This tutorial describes the underlying principles as well as advantages and limitations of existing models. It also presents new developments, thus serving as a guide to an appropriate usage of the multitude of current and emerging speech quality models.