Biology subjects

Das, N.

Publications and source records attributed to Das, N..

24 records · Page 2Linked to original sources

bioRxiv2020

EEG-informed speaker extraction from noisy recordings in neuro-steered hearing aids: linear versus deep learning methods

ObjectiveA hearing aids noise reduction algorithm cannot infer to which speaker the user intends to listen to. Auditory attention decoding (AAD) algorithms allow to infer this information from neural signals, which leads to the concept of neuro-steered hearing aids. We aim to evaluate and demonstrate the feasibility of AAD-supported speech enhancement in challenging noisy conditions based on electroencephalography (EEG) recordings. ApproachThe AAD performance with a linear versus a deep neural network (DNN) based speaker separation was evaluated for same-gender speaker mixtures using 3 different speaker positions and 3 different noise conditions. Main resultsAAD results based on the linear approach were found to be at least on par and sometimes even better than pure DNN-based approaches in terms of AAD accuracy in all tested conditions. However, when using the DNN to support a linear data-driven beamformer, a performance improvement over the purely linear approach was obtained in the most challenging scenarios. The use of multiple microphones was also found to improve speaker separation and AAD performance over single-microphone systems. SignificanceRecent proof-of-concept studies in this context each focus on a different method in a different experimental setting, which makes it hard to compare them. Furthermore, they are tested in highly idealized experimental conditions, which are still far from a realistic hearing aid setting. This work provides a systematic comparison of a linear and non-linear neuro-steered speech enhancement model, as well as a more realistic validation in challenging conditions.

bioengineering↗

bioRxiv2019

3dSpAn: an interactive software for 3D segmentation and analysis of dendritic spines

Three dimensional segmentation and analysis of dendritic spines involve two major challenges: 1) how to segment individual spines from the dendrites and 2) how to quantitatively assess the morphology of individual spines. We developed a software named 3dSpAn to address these two issues by implementing our previously published 3D multiscale opening algorithm in shared intensity space and using effective morphological features for individual dendritic spine plasticity analysis. 3dSpAn consists of four modules: Preprocessing and ROI selection, Intensity thresholding and seed selection, Multiscale segmentation and Quantitative morphological feature extraction. We show the results of segmentation and morphological analysis for different observation methods, including in vitro and ex vivo imaging with confocal microscopy, and in vivo samples, using high-resolution two-photon microscopy. The software is freely available, the source code, windows installer, the software manual and video tutorial can be obtained from: https://sites.google.com/view/3dSpAn/.

neuroscience↗

bioRxiv2019

Down-regulated GDF9 and BMP15 mRNAs are the important determining factors for the unexplained female infertility cases advised for assisted reproductive technology

BackgroundIn India, the potential risk factors influencing female infertility are not well-characterized. Hence, this study was initially aimed to determine the multi-factor risk-predictive models for the infertile females from different Indian ethnics. Afterwards, when treatment was performed for correcting the abnormal demographic and clinical parameters obtained from our initial findings, some of the patients were observed to be treatment-non-responsive. This further recommended assessing the expression pattern of GDF9 and BMP15 mRNAs in those unexplained infertile females. MethodologyThis study included 470 Primary, 209 Secondary infertile and 419 age-matched Healthy females, where baseline demographic and clinicopathological factors were assessed. Additionally, from the treatment-non-responsive patients, laparoscopy and trans-vaginal ultrasound-guided collection of ovarian follicular fluid were done for studying the expression pattern of GDF9 and BMP15 mRNAs using q-PCR. Principal FindingsMid-education level, no contraception usage and duration of infertility for >2 years were the critical risk factors for female infertility (OR>>1; p<0.05), whereas elevated platelet count showed ~10-fold more risk of having secondary infertility and high ESR showed 3.33-fold increased threat of primary infertility. Besides, abnormal MCV, LH/FSH ratio, mild-anaemia and hypothyroidism were the best risk-predictive multi-factor model. After correcting these abnormal clinical factors, 6.8% of the patients were treatment-non-responsive, where elevated ESR, hypothyroidism and anaemia remained uncorrected. Interestingly, in those unexplained cases, GDF9 and BMP15 mRNAs were significantly down-regulated (2-4 fold) irrespective of age groups. Additionally, for predicting female infertility, the cut-off for the expression level of GDF9 and BMP15 mRNAs were estimated -2.09 and -2.75, respectively, with a relatively high sensitivity ([≥]62.5%) and specificity (75%). ConclusionsGDF9 and BMP15 mRNAs may be considered as molecular markers for predicting female infertility. Their down-regulation along with hypothyroidism and anaemia probably led to the treatment-non-responsiveness, thus, those patients may be advised to assisted reproductive technology with supplementation of exogenous GDF9 and/or BMP15.

epidemiology↗

bioRxiv2019

Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

Objective. Neural responses recorded using electroencephalography (EEG) and magnetoencephalography (MEG) can be used to study how our brain functions, as well as for various promising brain computer interface (BCI) applications. However, a common problem is the low signal to noise ratio (SNR) which makes it challenging to estimate task-related neural responses or the temporal response function (TRF) describing the linear relationship between the stimulus and the neural response, particularly over short data windows. To address these, we present an algorithm that takes advantage of the multi-channel nature of the recordings, and knowledge of the presented stimulus, to achieve a joint noise reduction and dimensionality reduction using spatial filtering. Methods. Forward modeling is used to project the stimulus onto the electrode space. The second-order statistics of this estimated desired signal and the raw neural data are used to estimate spatial filters that maximize the SNR of the neural response, based on a generalized eigenvalue decomposition. Main Results. 1. For synthesized EEG data, over a range of SNRs, our filtering resulted in significantly better TRF estimates from 20 s trials, compared to unfiltered EEG data. 2. On a dataset from 28 subjects who listened to a single-talker stimulus, our method resulted in correlations between predicted neural responses and the original EEG data that were significantly higher compared to standard approaches. 3. On a dataset of 16 subjects attending to 1 speaker in a two-speaker scenario, our method resulted in attention decoding accuracies which were higher compared to existing forward modelling methods. Significance. Our algorithm presents a data-driven way to denoise and reduce dimensionality of neural data, thus aiding further analysis, by utilizing the knowledge of the stimulus. The method is computationally efficient, and does not require repeated trials, thereby relieving experiment design from the necessity of presenting repeated stimuli to the subjects.

neuroscience↗

bioRxiv2018

EEG-based detection of the attended speaker and the locus of auditory attention with convolutional neural networks

In a multi-speaker scenario, the human auditory system is able to attend to one particular speaker of interest and ignore the others. It has been demonstrated that it is possible to use electroencephalography (EEG) signals to infer to which speaker someone is attending by relating the neural activity to the speech signals. However, classifying auditory attention within a short time interval remains the main challenge. We present a convolutional neural network-based approach to extract the locus of auditory attention (left/right) without knowledge of the speech envelopes. Our results show that it is possible to decode the locus of attention within 1 to 2 s, with a median accuracy of around 81%. These results are promising for neuro-steered noise suppression in hearing aids, in particular in scenarios where per-speaker envelopes are unavailable.

neuroscience↗

bioRxiv2018

Bootstrapping EEG-based auditory attention detection systems: boundary conditions for background noise and speaker positions

ObjectiveA listeners neural responses can be decoded to identify the speaker the person is attending to in a cocktail party environment. Such auditory attention detection methods have the potential to provide noise suppression algorithms in hearing devices with information about the listeners attention. A challenge is the effect of noise and other acoustic conditions that can reduce the attention detection accuracy. Specifically, noise can impact the ability of the person to segregate the sound sources and perform selective attention, as well as the external signal processing necessary to decode the attention effectively. The aim of this work is to systematically analyze the effect of noise level and speaker position on attention decoding accuracy.\n\nApproach28 subjects participated in the experiment. Auditory stimuli consisted of stories narrated by different speakers from 2 different locations, along with surrounding multi-talker background babble. EEG signals of the subjects were recorded while they focused on one story and ignored the other. The strength of the babble noise as well as the spatial separation between the two speakers were varied between presentations. Spatio-temporal decoders were trained for each subject, and applied to decode attention of the subjects from every 30s segment of data. Behavioral speech recognition thresholds were obtained for the different speaker separations.\n\nMain resultsBoth the background noise level and the angular separation between speakers affected attention decoding accuracy. Remarkably, attention decoding performance was seen to increase with the inclusion of moderate background noise (versus no noise), while across the different noise conditions performance dropped significantly with increasing noise level. We also observed that decoding accuracy improved with increasing speaker separation, exhibiting the advantage of spatial release from masking. Furthermore, the effect of speaker separation on the decoding accuracy became stronger when the background noise level increased. A significant correlation between speech intelligibility and attention decoding accuracy was found across conditions.\n\nSignificanceThis work shows how the background noise level and relative positions of competing talkers impact attention decoding accuracy. It indicates in which circumstances a neuro-steered noise suppression system may need to operate, in function of acoustic conditions. It also indicates the boundary conditions for the operation of EEG-based attention detection systems in neuro-steered hearing prostheses.\n\nIndex TermsAuditory attention detection, EEG processing, neuro-steered auditory prostheses, brain-computer interface, cocktail party, acoustic conditions.\n\nThe work is funded by KU Leuven Special Research Fund C14/16/057 and OT/14/119, FWO project nrs. 1.5.123.16N and G0A4918N, the ERC (637424) under the European Unions Horizon 2020 research and innovation programme, and a research gift of Starkey Hearing Technologies. The scientific responsibility is assumed by its authors.

bioengineering↗