Trial reportPloS one2016
Remembered or Forgotten?-An EEG-Based Computational Prediction Approach.
Trial report in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Decoding declarative memory process for predicting memory retrieval based on source localization.PloS one · 2022Article
- Prediction of Successful Memory Encoding Based on Lateral Temporal Cortical Gamma Power.Frontiers in neuroscience · 2021Article
- Expert-Level Intracranial Electroencephalogram Ictal Pattern Detection by a Deep Learning Neural Network.Frontiers in neurology · 2021Article
- Prediction of Visual Memorability with EEG Signals: A Comparative Study.Sensors (Basel, Switzerland) · 2020Article
- Single-Trial EEG Connectivity of Default Mode Network Before and During Encoding Predicts Subsequent Memory Outcome.Frontiers in systems neuroscience · 2020Article
- Odor Recognition with a Spiking Neural Network for Bioelectronic Nose.Sensors (Basel, Switzerland) · 2019Article
- Machine learning for MEG during speech tasks.Scientific reports · 2019Article
- The Role of Meaning in Visual Memory: Face-Selective Brain Activity Predicts Memory for Ambiguous Face Stimuli.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification.Sensors (Basel, Switzerland) · 2018Article
- Rapid Decoding of Hand Gestures in Electrocorticography Using Recurrent Neural Networks.Frontiers in neuroscience · 2018Article
- Deep learning with convolutional neural networks for EEG decoding and visualization.Human brain mapping · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of memory performance (remembered or forgotten) has various potential applications not only for knowledge learning but also for disease diagnosis. Recently, subsequent memory effects (SMEs)-the statistical differences in electroencephalography (EEG) signals before or during learning between subsequently remembered and forgotten events-have been found. This finding indicates that EEG signals convey the information relevant to memory performance. In this paper, based on SMEs we propose a computational approach to predict memory performance of an event from EEG signals. We devise a convolutional neural network for EEG, called ConvEEGNN, to predict subsequently remembered and forgotten events from EEG recorded during memory process. With the ConvEEGNN, prediction of memory performance can be achieved by integrating two main stages: feature extraction and classification. To verify the proposed approach, we employ an auditory memory task to collect EEG signals from scalp electrodes. For ConvEEGNN, the average prediction accuracy was 72.07% by using EEG data from pre-stimulus and during-stimulus periods, outperforming other approaches. It was observed that signals from pre-stimulus period and those from during-stimulus period had comparable contributions to memory performance. Furthermore, the connection weights of ConvEEGNN network can reveal prominent channels, which are consistent with the distribution of SME studied previously.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.