233 citations · 237 across the 5 of their papers we have counts for
5 papers
EEG-X: Device-Agnostic and Noise-Robust Foundation Model for EEG
Navid Mohammadi Foumani, Soheila Ghane, Nam Nguyen +3
Foundation models for EEG analysis are still in their infancy, limited by two key challenges: (1) variability across datasets caused by differences in recording devices and configu…
KnowEEG: Explainable Knowledge Driven EEG Classification
Amarpal Sahota, Navid Mohammadi Foumani, Raul Santos-Rodriguez +1
Electroencephalography (EEG) is a method of recording brain activity that shows significant promise in applications ranging from disease classification to emotion detection and bra…
MONSTER: Monash Scalable Time Series Evaluation Repository
Angus Dempster, Navid Mohammadi Foumani, Chang Wei Tan +6
We introduce MONSTER-the MONash Scalable Time Series Evaluation Repository-a collection of large datasets for time series classification. The field of time series classification ha…
Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series Classification
Navid Mohammadi Foumani, Chang Wei Tan, Geoffrey I. Webb +2
We argue that time series analysis is fundamentally different in nature to either vision or natural language processing with respect to the forms of meaningful self-supervised lear…
Improving Position Encoding of Transformers for Multivariate Time Series Classification
Navid Mohammadi Foumani, Chang Wei Tan, Geoffrey I. Webb +1
Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require effective position encoding to…