E-resources
Peer reviewed
-
Zhang, Zhi; Zhong, Sheng-hua; Liu, Yan
Expert systems with applications, 09/2024, Volume: 249Journal Article
With deep learning (DL) development, EEG-based emotion recognition has attracted increasing attention. Diverse DL algorithms emerge and intelligently decode human emotion from EEG signals. However, the lack of a toolbox encapsulating these techniques hampers further the design, development, testing, implementation, and management of intelligent systems. To tackle this bottleneck, we propose a Python toolbox, TorchEEGEMO, which divides the workflow into five modules: datasets, transforms, model_selection, models, and trainers. Each module includes plug-and-play functions to construct and manage a stage in the workflow. Recognizing the frequent access to time windows of interest, we introduce a window-centric parallel input/output system, bolstering the efficiency of DL systems. We finally conduct extensive experiments to provide the benchmark results of supported modules. Our extensive experimental results demonstrate the versatility and applicability of TorchEEGEMO across various scenarios. •The first deep learning toolbox towards EEG-based emotion recognition.•A workflow that divides the recognition system into five plug-and-play modules.•Built-in functions cover datasets, transformations, models, algorithms, and more.•A novel window-centric EEG I/O is to enhance system effectiveness.•Experiments demonstrate benchmark performance across various scenarios.
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
If the library membership card is not in the list,
add a new one.
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|
Source: Personal bibliographies
and: SICRIS
The material is available in full text. If you wish to order the material anyway, click the Continue button.