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Found 6 papers, 0 papers with code
Epileptic seizure forecasting with long short-term memory (LSTM) neural networks
Monitoring the change in EEG features over time allowed our model to produce good results over a range of different window sizes, which is an improvement on previous models and raises the possibility of altering the forecast to meet individual patient needs.
Path Signatures for Seizure Forecasting
In computational neuroscience, the prediction of future epileptic seizures from brain activity measurements, using EEG data, remains largely unresolved despite much dedicated research effort.
Autoregressive models for biomedical signal processing
Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering.
Brain Model State Space Reconstruction Using an LSTM Neural Network
Approach An LSTM filter was trained on simulated EEG data generated by a neural mass model using a wide range of parameters.
Frequency Superposition -- A Multi-Frequency Stimulation Method in SSVEP-based BCIs
The steady-state visual evoked potential (SSVEP) is one of the most widely used modalities in brain-computer interfaces (BCIs) due to its many advantages.
Multi-Frequency Canonical Correlation Analysis (MFCCA): A Generalised Decoding Algorithm for Multi-Frequency SSVEP
Stimulation methods that utilise more than one stimulation frequency have been developed for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) with the purpose of increasing the number of targets that can be presented simultaneously.
