MNEflow: Neural networks for EEG/MEG decoding and interpretation

نویسندگان

چکیده

MNEflow is a Python package for applying deep neural networks to multichannel electroencephalograpic (EEG) and magnetoencephalographic (MEG) measurements. This software comprises Tensorflow-based implementations of several popular convolutional network (CNN) models EEG–MEG data introduces flexible pipeline enabling easy application the most common preprocessing, validation, model interpretation approaches. The aims save time computational resources required analysis EEG MEG data.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

TCM Decoding Using Neural Networks

This paper presents a neural decoder for trellis coded modulation (TCM) schemes. Decoding is performed with Radial Basis Function Networks and Multi-Layer Perceptrons. The neural decoder effectively implements an adaptive Viterbi algorithm for TCM which learns communication channel imperfections. The implementation and performance of the neural decoder for trellis encoded 16-QAM with amplitude ...

متن کامل

Decoding neuronal firing and modelling neural networks.

Biological neural networks are large systems of complex elements interacting through a complex array of connections. Individual neurons express a large number of active conductances (Connors et al., 1982; Adams & Gavin, 1986; Llinás, 1988; McCormick, 1990; Hille, 1992) and exhibit a wide variety of dynamic behaviors on time scales ranging from milliseconds to many minutes (Llinás, 1988; Harris-...

متن کامل

Cellular Associative Neural Networks for Image Interpretation

IMAGE INTERPRETATION C Orovas, J Austin University of York, UK ABSTRACT This paper describes the architecture and the operation of a neural network based system for image interpretation. The system is based on the use of two models of associative neural networks, ADAM and AURA for image and symbolic processing respectively. Employing characteristics of cellular automata theory and applying idea...

متن کامل

Interpretation of Neural Networks for Classification Tasks

To overcome the black box behaviour of neural networks many different approaches have been proposed. Up to now there is no standard tool being able to handle general feedforward networks. In this paper a method is proposed to combine visualization techniques with transformation algorithms to interpret neural feedforward networks. An application in ultrasonic crack detection will show that the o...

متن کامل

Neural Networks for Text Correction and Completion in Keyboard Decoding

Despite the ubiquity of mobile and wearable text messaging applications, the problem of keyboard text decoding is not tackled sufficiently in the light of the enormous success of the deep learning Recurrent Neural Network (RNN) and Convolutional Neural Networks (CNN) for natural language understanding. In particular, considering that the keyboard decoders should operate on devices with memory a...

متن کامل

ذخیره 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: SoftwareX

سال: 2022

ISSN: ['2352-7110']

DOI: https://doi.org/10.1016/j.softx.2021.100951