EEG-MAE 自監督預訓練與個人化左右腳分類

完整 CUDA 結果

目前狀態:預訓練受試者 10 位、train pseudo-trials 11,593;正式微調受試者 10 位、左右腳 trials 2,009。
輸入固定為 32 通道 × 400 samples;比較 SSL full fine-tune、linear probe 與 scratch。

資料快取

dataset subject_id split kept_windows excluded_windows left_trials right_trials
pretrain sub16 train 1164 0 - -
pretrain sub16 val 125 0 - -
pretrain sub17 train 1146 0 - -
pretrain sub17 val 124 0 - -
pretrain sub18 train 1147 0 - -
pretrain sub18 val 123 0 - -
pretrain sub19 train 1139 0 - -
pretrain sub19 val 123 0 - -
pretrain sub20 train 1170 0 - -
pretrain sub20 val 126 0 - -
pretrain sub21 train 1158 0 - -
pretrain sub21 val 125 0 - -
pretrain sub22 train 1136 0 - -
pretrain sub22 val 123 0 - -
pretrain sub23 train 1154 0 - -
pretrain sub23 val 125 0 - -
pretrain sub47 train 1116 0 - -
pretrain sub47 val 120 0 - -
pretrain sub49 train 1263 0 - -
pretrain sub49 val 136 0 - -
labeled sub1 all 203 0 100.0 103.0
labeled sub2 all 203 0 102.0 101.0
labeled sub3 all 200 0 100.0 100.0
labeled sub9 all 201 0 100.0 101.0
labeled sub10 all 200 0 100.0 100.0
labeled sub11 all 200 0 100.0 100.0
labeled sub12 all 201 0 100.0 101.0
labeled sub13 all 200 0 100.0 100.0
labeled sub14 all 200 0 100.0 100.0
labeled sub15 all 201 0 100.0 101.0

CPU smoke test

狀態:passed;遮罩數 [96, 96];模型參數 3,126,032。

預訓練

Pretraining loss history

遮罩重建

Reconstruction waveformsReconstruction spectrum

個人化分類結果

Subject accuracyGroup accuracyConfusion matrices

群組摘要

mode n_subjects mean_accuracy std_accuracy mean_macro_f1 std_macro_f1
ssl_finetune 10 65.97% 12.64% 65.22% 13.30%
linear_probe 10 57.90% 9.05% 57.43% 9.30%
scratch 10 54.18% 6.84% 50.62% 8.70%

單一受試者摘要

subject_id mode mean_accuracy std_accuracy mean_macro_f1 std_macro_f1
sub1 linear_probe 54.06% 13.42% 53.54% 13.35%
sub1 scratch 49.21% 6.73% 40.97% 8.67%
sub1 ssl_finetune 68.98% 7.81% 68.55% 8.01%
sub10 linear_probe 56.50% 8.77% 56.40% 8.76%
sub10 scratch 52.00% 3.26% 47.73% 7.19%
sub10 ssl_finetune 79.50% 11.51% 79.46% 11.54%
sub11 linear_probe 72.50% 3.95% 72.40% 4.06%
sub11 scratch 51.00% 4.87% 48.14% 7.13%
sub11 ssl_finetune 86.50% 5.18% 86.43% 5.19%
sub12 linear_probe 53.30% 10.86% 51.71% 12.78%
sub12 scratch 54.23% 5.13% 50.45% 10.72%
sub12 ssl_finetune 62.76% 11.73% 61.70% 13.26%
sub13 linear_probe 75.50% 5.12% 75.35% 5.31%
sub13 scratch 69.50% 8.91% 68.67% 9.39%
sub13 ssl_finetune 78.50% 9.45% 78.38% 9.54%
sub14 linear_probe 51.00% 5.48% 50.77% 5.60%
sub14 scratch 50.00% 7.71% 49.36% 7.56%
sub14 ssl_finetune 52.50% 8.66% 51.30% 8.07%
sub15 linear_probe 49.24% 7.27% 48.87% 7.13%
sub15 scratch 50.76% 3.91% 44.10% 8.66%
sub15 ssl_finetune 47.72% 11.37% 45.33% 14.06%
sub2 linear_probe 50.76% 5.23% 49.50% 5.07%
sub2 scratch 47.30% 2.34% 41.93% 5.91%
sub2 ssl_finetune 57.12% 8.87% 56.10% 9.80%
sub3 linear_probe 57.00% 7.98% 56.72% 8.15%
sub3 scratch 62.50% 6.85% 61.64% 8.29%
sub3 ssl_finetune 68.00% 6.47% 67.80% 6.59%
sub9 linear_probe 59.17% 7.39% 59.04% 7.26%
sub9 scratch 55.27% 8.09% 53.18% 8.51%
sub9 ssl_finetune 58.17% 10.06% 57.17% 10.01%

成對比較

first_mode second_mode n_subjects first_mean_accuracy second_mean_accuracy mean_paired_difference bootstrap_ci95_low bootstrap_ci95_high wilcoxon_statistic wilcoxon_p_value
ssl_finetune scratch 10 65.97% 54.18% 11.80% 5.23% 19.38% 3.0 0.009766
ssl_finetune linear_probe 10 65.97% 57.90% 8.07% 3.58% 12.73% 4.0 0.013672

輸出檔案

data_manifest.csvfold_assignments.csvfold_results.csvgroup_summary.csvpaired_comparisons.csvpretrain_history.csvreconstruction_examples.npzsmoke_test.jsonsubject_summary.csv