Multimodal resting-state connectivity predicts affective neurofeedback performance.

Neurofeedback has been suggested as a potential complementary therapy to different psychiatric disorders. Of interest for this approach is the prediction of individual performance and outcomes. In this study, we applied functional connectivity-based modeling using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) modalities to (i) investigate whether resting-state connectivity predicts performance during an affective neurofeedback task and (ii) evaluate the extent to which predictive connectivity profiles are correlated across EEG and fNIRS techniques. The fNIRS oxyhemoglobin and deoxyhemoglobin concentrations and the EEG beta and gamma bands modulated by the alpha frequency band (beta-m-alpha and gamma-m-alpha, respectively) recorded over the frontal cortex of healthy subjects were used to estimate functional connectivity from each neuroimaging modality. For each connectivity matrix, relevant edges were selected in a leave-one-subject-out procedure, summed into "connectivity summary scores" (CSS), and submitted as inputs to a support vector regressor (SVR). Then, the performance of the left-out-subject was predicted using the trained SVR model. Linear relationships between the CSS across both modalities were evaluated using Pearson's correlation. The predictive model showed a mean absolute error smaller than 20%, and the fNIRS oxyhemoglobin CSS was significantly correlated with the EEG gamma-m-alpha CSS (r = -0.456, p = 0.030). These results support that pre-task electrophysiological and hemodynamic resting-state connectivity are potential predictors of neurofeedback performance and are meaningfully coupled. This investigation motivates the use of joint EEG-fNIRS connectivity as outcome predictors, as well as a tool for functional connectivity coupling investigation.

Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces.

Based on recent electroencephalography (EEG) and near-infrared spectroscopy (NIRS) studies that showed that tasks such as motor imagery and mental arithmetic induce specific neural response patterns, we propose a hybrid brain-computer interface (hBCI) paradigm in which EEG and NIRS data are fused to improve binary classification performance. We recorded simultaneous NIRS-EEG data from nine participants performing seven mental tasks (word generation, mental rotation, subtraction, singing and navigation, and motor and face imagery). Classifiers were trained for each possible pair of tasks using (1) EEG features alone, (2) NIRS features alone, and (3) EEG and NIRS features combined, to identify the best task pairs and assess the usefulness of a multimodal approach. The NIRS-EEG approach led to an average increase in peak kappa of 0.03 when using features extracted from one-second windows (equivalent to an increase of 1.5% in classification accuracy for balanced classes). The increase was much stronger (0.20, corresponding to an 10% accuracy increase) when focusing on time windows of high NIRS performance. The EEG and NIRS analyses further unveiled relevant brain regions and important feature types. This work provides a basis for future NIRS-EEG hBCI studies aiming to improve classification performance toward more efficient and flexible BCIs.

MS-QI: A Modulation Spectrum-Based ECG Quality Index for Telehealth Applications.

As telehealth applications emerge, the need for accurate and reliable biosignal quality indices has increased. One typical modality used in remote patient monitoring is the electrocardiogram (ECG), which is inherently susceptible to several different noise sources, including environmental (e.g., powerline interference), experimental (e.g., movement artifacts), and physiological (e.g., muscle and breathing artifacts). Accurate measurement of ECG quality can allow for automated decision support systems to make intelligent decisions about patient conditions. This is particularly true for in-home monitoring applications, where the patient is mobile and the ECG signal can be severely corrupted by movement artifacts. In this paper, we propose an innovative ECG quality index based on the so-called modulation spectral signal representation. The representation quantifies the rate of change of ECG spectral components, which are shown to be different from the rate of change of typical ECG noise sources. The proposed modulation spectral-based quality index, MS-QI, was tested on 1) synthetic ECG signals corrupted by varying levels of noise, 2) single-lead recorded data using the Hexoskin garment during three activity levels (sitting, walking, running), 3) 12-lead recorded data using conventional ECG machines (Computing in Cardiology 2011 dataset), and 4) two-lead ambulatory ECG recorded from arrhythmia patients (MIT-BIH Arrhythmia Database). Experimental results showed the proposed index outperforming two conventional benchmark quality measures, particularly in the scenarios involving recorded data in real-world environments.

A model that predicts the binaural advantage to speech intelligibility from the mixed target and interferer signals.

A model is presented that predicts the binaural advantage to speech intelligibility by analyzing the right and left recordings at the two ears containing mixed target and interferer signals. This auditory-inspired model implements an equalization-cancellation stage to predict the binaural unmasking (BU) component, in conjunction with a modulation-frequency estimation block to estimate the "better ear" effect (BE) component of the binaural advantage. The model's performance was compared to experimental data obtained under anechoic and reverberant conditions using a single speech-shaped noise interferer paradigm. The internal BU and BE components were compared to those of the speech intelligibility model recently proposed by Lavandier et al. [J. Acoust. Soc. Am. 131, 218-231 (2012)], which requires separate inputs for target and interferer. The data indicate that the proposed model provides comparably good predictions from a mixed-signals input under both anechoic and reverberant conditions.

Performance measurement for brain-computer or brain-machine interfaces: a tutorial.

OBJECTIVE: Brain-computer interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research. APPROACH: A workshop on this topic was held at the 2013 International BCI Meeting at Asilomar Conference Center in Pacific Grove, California. This paper contains the consensus opinion of the workshop members, refined through discussion in the following months and the input of authors who were unable to attend the workshop. MAIN RESULTS: Checklists for methods reporting were developed for both discrete and continuous BCIs. Relevant metrics are reviewed for different types of BCI research, with notes on their use to encourage uniform application between laboratories. SIGNIFICANCE: Graduate students and other researchers new to BCI research may find this tutorial a helpful introduction to performance measurement in the field.

On a reference-free speech quality estimator for hearing aids.

A reference-free speech quality measure is proposed and assessed for hearing aid applications. The proposed speech quality metric is validated with subjective ratings obtained from hearing impaired listeners under a number of noisy and reverberant conditions. In addition, a comparison is drawn between the proposed measure and a state-of-the-art electroacoustic measure that relies on a clean reference signal. The results showed that the reference-free measure had a lower correlation with the subjective ratings of hearing aid speech quality in comparison to the correlations achieved by the measure utilizing a reference signal. Nevertheless, advantages of the reference-free approach are discussed.

Taking NIRS-BCIs outside the lab: towards achieving robustness against environment noise.

This paper reported initial findings on the effects of environmental noise and auditory distractions on the performance of mental state classification based on near-infrared spectroscopy (NIRS) signals recorded from the prefrontal cortex. Characterization of the performance losses due to environmental factors could provide useful information for the future development of NIRS-based brain-computer interfaces that can be taken beyond controlled laboratory settings and into everyday environments. Experiments with a hidden Markov model-based classifier showed that while significant performance could be attained in silent conditions, only chance levels of sensitivity and specificity were obtained in noisy environments. In order to achieve robustness against environment noise, two strategies were proposed and evaluated. First, physiological responses harnessed from the autonomic nervous system were used as complementary information to NIRS signals. More specifically, four physiological signals (electrodermal activity, skin temperature, blood volume pulse, and respiration effort) were collected in synchrony with the NIRS signals as the user sat at rest and/or performed music imagery tasks. Second, an acoustic monitoring technique was proposed and used to detect startle noise events, as both the prefrontal cortex and ANS are known to involuntarily respond to auditory startle stimuli. Experiments with eight participants showed that with a startle noise compensation strategy in place, performance comparable to that observed in silent conditions could be recovered with the hybrid ANS-NIRS system.

Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy.

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.