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Task 2 - Emotion Recognition

In this task participants are asked to infer the emotional category of EEG recordings.

We have 4 emotional labels:

Test Dataset.

The test set consists of two parts:

The list of ids to use for inference is in the splits_emotion_recognition.json file.

Target metric

The target metric is the average balanced accuracy score between the two test sets. This means we will compute separately the balanced accuracy score on both held-out-trials and held-out-subjects test sets. These two scores will be averaged together to obtain the final score.

Baseline implementation

We used pruned data as starting point and we applied minimal preprocessing involving:

We chose 3 well-known architectures as baselines

No significant changes were applied to the original architectures.

We created two small validation sets:

Training and inference was performed as in task 1 except that we used a majority voting, instead of the average logits.

The final model and hyperparameters (learning rate, batch size) were selected based on the target metric on our valdation sets. A grid search was conducted to optimize these parameters.

Leaderboard

Team Held-out-trialr Bal. Acc. Held-out-subject Bal. Acc. Total Score
iBrain 51.39 52.56 51.97
TUM 46.53 36.15 41.34
BCIGO 46.69 34.9 40.79
GISP@HEU 44.65 31.95 38.3
MindReader 40.88 34.81 37.85
Neural Harmony 38.22 34.4 36.31
SAIL 43.54 25.43 34.49
KONKUK_AICV 26.71 35.89 31.3
WisdomDLUT 32.83 28.35 30.59
EEGNet [1] 30.72 29.47 30.10
ZeD 28.21 31.46 29.83
NTUA-IRAL 27.43 27.9 27.66
Hunan 29.57 23.25 26.41
EMER_IITMZ 24.94 27.32 26.13
EEGChannelNet [3] 29.76 22.23 26.00
SyncNet [2] 28.80 23.08 25.94
WCQY 24.08 24.88 24.48
IIP-HCI 22.94 24.47 23.7
SIP Lab-IITH 21.34 25.83 23.58
CUSAP2 21.55 24.91 23.23
CherryBlossoms 21.31 24.27 22.79
NWPU EEG 21.87 23.38 22.63
TEAS 19.63 23.37 21.5
btbu-713 19.2 23.12 21.16
NJUST_KMC 10.77 26.96 18.87

Results for [1], [2] and [3] come from our baselines. You can replicate results using our GitHub repository.


[1] V j Lawhern et al., “EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces”, J. Neural Eng. 15/5, 2018.

[2] Y. Li et al, “Targeting EEG/LFP Synchrony with Neural Nets”, NeurIPS 2017.

[3] S. Palazzo, C. Spampinato, I. Kavasidis, D. Giordano, J. Schmidt and M. Shah, ““Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features”, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 11, 2021.