Calibration-Free BCI Based Control
Authors: Jonathan Grizou, Iñaki Iturrate, Luis Montesano, Pierre-Yves Oudeyer, Manuel Lopes
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report experiments where four users use BCI to control an agent on a virtual world to reach a target without any previous calibration process. |
| Researcher Affiliation | Academia | Jonathan Grizou Inria Bordeaux Sud-Ouest, France jonathan.grizou@inria.fr Inaki Iturrate CNBI, EPFL, Switzerland inaki.iturrate@epfl.ch Luis Montesano I3A, Univ. of Zaragoza, Spain montesano@unizar.es Pierre-Yves Oudeyer Inria Bordeaux Sud-Ouest, France pierre-yves.oudeyer@inria.fr Manuel Lopes Inria Bordeaux Sud-Ouest, France manuel.lopes@inria.fr |
| Pseudocode | No | The paper describes the algorithm formally through equations and prose but does not include any explicit pseudocode blocks or sections labeled "Algorithm". |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | Yes | We used a dataset from (Iturrate, Montesano, and Minguez 2013b), which covers ten subjects that performed two different control problems (denoted T1 and T2). |
| Dataset Splits | No | The paper mentions "cross-validation" as a possible solution for estimating classification rates but does not provide specific train/validation/test dataset splits (percentages or counts) used for their experiments. |
| Hardware Specification | No | The paper mentions "EEG signals were recorded with a g Tec system (2 g USBamp amplifiers)" for data acquisition, but it does not provide specific hardware details (like GPU/CPU models, processor types, or memory amounts) used for running the computational experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | The user goal is to teach the agent to reach one, yet unknown to the agent, of the 25 discrete states..., EEG signals were recorded with a g Tec system..., sampling frequency of 256 Hz, common-average-reference (CAR) filtered and band-pass filtered at [0.5, 10] Hz., features were extracted from two frontocentral channels (FCz and Cz) within a time window of [200, 700] ms... and downsampled to 32 Hz. This leaded to a vector of 34 features., modeling the assessment error rate of the user, which was set to 0.1 for our experiments., λ the regularization term which was set to 0.5 for our experiments., We used β = 0.99., In practice, we limited D t i to the last 250 elements. |