4 accepted papers
Manual seizure detection from long recordings of the electroencephalogram (EEG) is a tiring, tedious, and error-prone process. It also requires experienced practitioners to detect seizure events precisely. This paper has proposed a novel method to detect epileptic seizures from long-EEG recordings u…
This paper reports on a novel invasive brain–computer interface (BCI) paradigm that has successfully reconstructed spoken sentences from invasive electrocorticogram (ECoG) signals using deep-neural-network-based encoders and a pre-trained neural vocoder. We recorded ECoG signals while 13 participant…
Automated identification of epileptiform discharges in electroencephalograms (EEG) for the diagnosis of epilepsy can mitigate the burden of manual searches. Recent effective methods based on machine learning–based classification have used detection of candidate waveforms with signal processing and p…
Invasive brain–machine interfaces (BMIs) are a promising neurotechnological venture for achieving direct speech communication from a human brain, but it faces many challenges. In this paper, we measured the invasive electrocorticogram (ECoG) signals from seven participating epilepsy patients as they…