Single Trial P300 Classification Using Convolutional LSTM and Deep Learning Ensembles Method

2018 ◽  
pp. 3-15 ◽  
Author(s):  
Raviraj Joshi ◽  
Purvi Goel ◽  
Mriganka Sur ◽  
Hema A. Murthy
Keyword(s):  
Deep Learning ◽  
Single Trial ◽  
Learning Ensembles ◽  
Convolutional Lstm

Speedup of deep learning ensembles for semantic segmentation using a model compression technique

2017 ◽  
Vol 164 ◽  
pp. 16-26 ◽  
Author(s):  
Andrew Holliday ◽  
Mohammadamin Barekatain ◽  
Johannes Laurmaa ◽  
Chetak Kandaswamy ◽  
Helmut Prendinger
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Compression Technique ◽  
Model Compression ◽  
Learning Ensembles

scholarly journals Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Amirali Vahid ◽  
Moritz Mückschel ◽  
Sebastian Stober ◽  
Ann-Kathrin Stock ◽  
Christian Beste
Keyword(s):  
Deep Learning ◽  
Action Control ◽  
Single Trial ◽  
Eeg Data

scholarly journals The hybrid nature of task-evoked activity: Inside-out neural dynamics in intracranial EEG and Deep Learning

2020 ◽  
Author(s):  
Annemarie Wolff ◽  
Liang Chen ◽  
Shankar Tumati ◽  
Mehrshad Golesorkhi ◽  
Javier Gomez-Pilar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reaction Times ◽  
Relative Effect ◽  
External Stimulus ◽  
Single Trial ◽  
Hybrid Nature ◽  
Evoked Activity ◽  
The Impact ◽  
Inside Out ◽  
Conceptual Advance

A.AbstractThe standard approach in neuroscience research infers from the external stimulus (outside) to the brain (inside) through stimulus-evoked activity. Recently challenged by Buzsáki, he advocates the reverse; an inside-out approach inferring from the brain’s activity to the neural effects of the stimulus. If so, stimulus-evoked activity should be a hybrid of internal and external components. Providing direct evidence for this hybrid nature, we measured human intracranial stereo-electroencephalography (sEEG) to investigate how prestimulus variability, i.e., standard deviation, shapes poststimulus activity through trial-to-trial variability. We first observed greater poststimulus variability quenching in trials exhibiting high prestimulus variability. Next, we found that the relative effect of the stimulus was higher in the later (300-600ms) than the earlier (0-300ms) poststimulus period. These results were extended by our Deep Learning LSTM network models at the single trial level. The accuracy to classify single trials (prestimulus low/high) increased greatly when the models were trained and tested with real trials compared to trials that exclude the effects of the prestimulus-related ongoing dynamics (corrected trials). Lastly, we replicated our findings showing that trials with high prestimulus variability in theta and alpha bands exhibits faster reaction times. Together, our results support the inside-out approach by demonstrating that stimulus-related activity is a hybrid of two factors: 1) the effects of the external stimulus itself, and 2) the effects of the ongoing dynamics spilling over from the prestimulus period, with the second, i.e., the inside, dwarfing the influence of the first, i.e., the outside.B.Significance StatementOur findings signify a significant conceptual advance in the relationship between pre- and poststimulus dynamics in humans. These findings are important as they show that we miss an essential component - the impact of the ongoing dynamics - when restricting our analyses to the effects of the external stimulus alone. Consequently, these findings may be crucial to fully understand higher cognitive functions and their impairments, as can be seen in psychiatric illnesses. In addition, our Deep Learning LSTM models show a second conceptual advance: high classification accuracy of a single trial to its prestimulus state. Finally, our replicated results in an independent dataset and task showed that this relationship between pre- and poststimulus dynamics exists across tasks and is behaviorally relevant.

Glaucoma classification via deep learning ensembles

2021 ◽  
Author(s):  
Ali Serener ◽  
Sertan Serte
Keyword(s):  
Deep Learning ◽  
Learning Ensembles

scholarly journals Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM

2020 ◽  
Vol 8 (10) ◽  
pp. 805
Author(s):  
Ki-Su Kim ◽  
June-Beom Lee ◽  
Myung-Il Roh ◽  
Ki-Min Han ◽  
Gap-Heon Lee
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Satellite Communication ◽  
Short Term Memory ◽  
Extended Period ◽  
Learning Model ◽  
Average Error ◽  
Denoising Autoencoder ◽  
Wide Range ◽  
Convolutional Lstm

The path planning of a ship requires much information, and one of the essential factors is predicting the ocean environment. Ocean weather can generally be gathered from forecasting information provided by weather centers. However, these data are difficult to obtain when satellite communication is unstable during voyages, or there are cases where forecast data for a more extended period of time are needed for the operation of the fleet. Therefore, shipping companies and classification societies have attempted to establish a model for predicting the ocean weather on its own. Historically, ocean weather has been primarily predicted using empirical and numerical methods. Recently, a method for predicting ocean weather using deep learning has emerged. In this study, a deep learning model combining a denoising AutoEncoder and convolutional long short-term memory (LSTM) was proposed to predict the ocean weather worldwide. The denoising AutoEncoder is effective for removing noise that hinders the training of deep learning models. While the LSTM could be used as time-series inputs at specific points, the convolutional LSTM can use time-series images as inputs, making them suitable for predicting a wide range of ocean weather. Herein, using the proposed model, eight parameters of ocean weather were predicted. The proposed learning model predicted ocean weather after one week, showing an average error of 6.7%. The results show the applicability of the proposed learning model for predicting ocean weather.

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