EPViz (EEG Prediction Visualizer)
Scalp EEG is one of the most popular modalities for studying real-time neural phenomena. While traditional EEG studies have focused on identifying group-level statistical effects, the rise of machine learning has prompted a shift in the community towards spatio-temporal predictive analyses. We have developed the EEG Prediction Visualizer (EPViz) to aid researchers in developing, validating, and reporting their predictive modeling outputs. EPViz is a lightweight and standalone software package developed in Python. Beyond viewing and manipulating the EEG data, EPViz allows user to load a PyTorch deep learning model, apply it to the EEG, and overlay the output channel-wise or subject-level temporal predictions on top of the original time series.
These results can be saved as high-resolution images for use in manuscripts and presentations. There is also a command-line option for batch processing. EPViz also provides valuable tools for clinician-scientists, including spectrum visualization, computation of basic statistics, data anonymization, and annotation editing.
EPViz can be installed in three ways: (1) cloning our GitHub repository to access the latest version, (2) through PyPI, and (3) as a standalone prepackaged application for MacOS and Windows. [github][pypi][Mac App][Windows App].
Please visit our EPViz page for the complete user guide.
EDF Anonymizer
In conjunction with EPViz, we have provided the EEG anonymization tool as a standalone module. This tool allows the user to alter the EDF header fields and provides default settings for scrubbing patient IDs and time stamps. [github][Windows App][MAC App]
Predictive Connectomics
- A Matrix Auto-encoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes
N.S. D’Souza, M.B. Nebel, D. Crocetti, N. Wymbs, J. Robinson, S. Mostofsky, A. Venkataraman
MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 12907, pp. 625-636 (2021) [paper][github] - M-GCN: A Multimodal Graph Convolutional Network to Integrate Functional and Structural Connectomics Data to Predict Multidimensional Phenotypic Characterizations
N.S. D’Souza, M.B. Nebel, D. Crocetti, N. Wymbs, J. Robinson, S. Mostofsky, A. Venkataraman
MIDL: Medical Imaging with Deep Learning, PMLR 143, pp. 119-130 (2021) [paper][github] - A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism
N.S. D’Souza, M.B. Nebel, D. Crocetti, N. Wymbs, J. Robinson, S. Mostofsky, A. Venkataraman
MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS 12267, pp. 437-447 (2020) [paper][github] - A Joint Network Optimization Framework to Predict Clinical Severity from Resting State fMRI Data
N.S. D’Souza, N. Wymbs, M.B. Nebel, S. Mostofsky, A. Venkataraman
NeuroImage, vol. 206, pp. 116314 (2020) [paper][github]
Imaging-Genetics
- A Generative Discriminative Framework that Integrates Imaging, Genetic, and Diagnosis into Coupled Low Dimensional Space
S. Ghosal, Q. Chen, G. Pergola, A.L. Goldman, W. Ulrich, K.F. Berman, A. Rampino, G. Blasi, L. Fazio, A. Bertolino, D.R. Weinberger, V.S. Mattay, A. Venkataraman
NeuroImage, vol. 238, pp. 118200 (2021) [paper][github]
Preoperative Mapping with Resting-State fMRI
- A Multi-Scale Spatial and Temporal Attention Network on Dynamic Connectivity to Localize the Eloquent Cortex in Brain Tumor Patients
N. Nandakumar, K. Manzoor, S. Agarwal, J. Pillai, S. Gujar, H. Sair, A. Venkataraman
IPMI: Information Processing in Medical Imaging, LNCS 12729, pp. 241-252 (2021) [paper][github] - A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients using Both Static and Dynamic Functional Connectivity
N. Nandakumar, N.S. D’Souza, K. Manzoor, J. Pillai, S. Gujar, S. Agarwal, H. Sair, A. Venkataraman
MLCN: MICCAI Workshop on Machine Learning for Clinical Neuroimaging, LNCS 12449, pp. 34-44 (2020) [paper][github]
Emotional Speech
- A Diffeomorphic Flow-based Variational Framework for Multi-speaker Emotion Conversion
R. Shankar, H.-W. Hsieh, N. Charon, A. Venkataraman
IEEE Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 39-53 (2023) [paper][github][sample audio] - Adaptive Duration Modification of Speech using Masked Convolutional Networks and Open-Loop Time Warping
R. Shankar and A. Venkataraman
ISCA Speech Synthesis Workshop, pp. 1-5 (2023) [paper][github] - Non-parallel Emotion Conversion using a Pair Discrimination Deep-Generative Hybrid Model
R. Shankar, J. Sager, A. Venkataraman
Interspeech: Conference of the International Speech Communication Association, pp. 3396-3400 (2020) [paper][github] - Multispeaker Emotion Conversion via a Chained Encoder-Decoder-Predictor Network and Latent Variable Regularization
R. Shankar, H.-W. Hsieh, N. Charon, A. Venkataraman
Interspeech: Conference of the International Speech Communication Association, pp. 3391-3395 (2020) [paper][github]
