MAtt: A Manifold Attention Network for EEG Decoding
Authors: Yue-Ting Pan, Jing-Lun Chou, Chun-Shu Wei
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The evaluation of the proposed MAtt on both time-synchronous and -asyncronous EEG datasets suggests its superiority over other leading DL methods for general EEG decoding. |
| Researcher Affiliation | Academia | Yue-Ting Pan Jing-Lun Chou Chun-Shu Wei National Yang Ming Chiao Tung University, Hsunchu, Taiwan wei@nycu.edu.tw |
| Pseudocode | Yes | Algorithm 1 Manifold attention module Require: A sequence of SPD data { xi}m i=1 , transformation matrices Wq, Wk, Wv 1: for i = 1: m do 2: qi = Wq xi W T q ; ki = Wk xi W T k ; vi = Wv xi W T v 3: end for 4: i, j {1, , m}, A := [αij]m m = 1 1+log(1+δL(qi,kj)) 5: A = Softmax(A) 6: for i = 1: m do 7: v i = Exp l=1 α il Log(vl) 8: end for 9: return a sequence of SPD data {v i}m i=1 |
| Open Source Code | Yes | Source codes are available at https://github.com/CECNL/MAtt. |
| Open Datasets | Yes | We incorporate the BCI Competition IV 2a Dataset (BCIC-IV-2a) [47] to assess the performance on time-asynchronous motor-imagery (MI) EEG decoding , the MAMEM EEG SSVEP Dataset II (MAMEM-SSVEP-II) [48] and the BCI challenge error-related negativity (ERN) dataset (BCI-ERN) [49] to assess the performance on time-synchronous SSVEP and ERN EEG decoding. |
| Dataset Splits | Yes | For the BCIC-IV-2a dataset, we used the first session of a subject to the training set where one out of eight was used for validation for MAtt with m = 3. The model with the lowest validation loss within 350 iterations was used for testing on the second session of the same subject. For the MAMEM-SSVEP-II/BCI-ERN dataset, we assigned the first four sessions of a subject to the training set where one out of four was used for validation for MAtt with m = 7/m = 3. The model with the lowest validation loss within 180/130 iterations was used for testing on the fifth session of the same subject. |
| Hardware Specification | No | The paper describes the EEG recording hardware used for data collection (e.g., EEG electrodes, EGI 300 Geodesic EEG System) but does not provide specific details about the computational hardware (e.g., GPU models, CPU types, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | Our parameter setting follows [19]. [...] We set ϵ as 1e-5 in our source code. [...] For the BCIC-IV-2a dataset, we used the first session of a subject to the training set where one out of eight was used for validation for MAtt with m = 3. The model with the lowest validation loss within 350 iterations was used for testing on the second session of the same subject. For the MAMEM-SSVEP-II/BCI-ERN dataset, we assigned the first four sessions of a subject to the training set where one out of four was used for validation for MAtt with m = 7/m = 3. The model with the lowest validation loss within 180/130 iterations was used for testing on the fifth session of the same subject. |