Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control
نویسندگان
چکیده
منابع مشابه
Interpretable Deep Neural Networks for Single-Trial EEG Classification
BACKGROUND In cognitive neuroscience the potential of deep neural networks (DNNs) for solving complex classification tasks is yet to be fully exploited. The most limiting factor is that DNNs as notorious 'black boxes' do not provide insight into neurophysiological phenomena underlying a decision. Layer-wise relevance propagation (LRP) has been introduced as a novel method to explain individual ...
متن کاملLearning Spatial and Temporal Filters for Single-Trial EEG Classification
There is a wide variety of electroencephalography (EEG) analysis methods. Most of them are based on averaging over multiple trials in order to increase signal-to-noise ratio. The method introduced in this article is a single trial method. Our approach is based on the assumption that the ”real brain signal” of each task is smooth, and is contained in several sensor channels. We propose two stage...
متن کاملApplying Deep Learning to the Newsvendor Problem
The newsvendor problem is one of the most basic and widely applied inventory models. There are numerous extensions of this problem. One important extension is the multi-item newsvendor problem, in which the demand of each item may be correlated with that of other items. If the joint probability distribution of the demand is known, the problem can be solved analytically. However, approximating t...
متن کاملApplying Deep Learning to Basketball Trajectories
One of the emerging trends for sports analytics is the growing use of player and ball tracking data. A parallel development is deep learning predictive approaches that use vast quantities of data with less reliance on feature engineering. This paper applies recurrent neural networks in the form of sequence modeling to predict whether a three-point shot is successful. The models are capable of l...
متن کاملMulti-Task Deep Reinforcement Learning for Continuous Action Control
In this paper, we propose a deep reinforcement learning algorithm to learn multiple tasks concurrently. A new network architecture is proposed in the algorithm which reduces the number of parameters needed by more than 75% per task compared to typical single-task deep reinforcement learning algorithms. The proposed algorithm and network fuse images with sensor data and were tested with up to 12...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Communications Biology
سال: 2020
ISSN: 2399-3642
DOI: 10.1038/s42003-020-0846-z