BMC Bioinformatics

Table 2 Classification accuracies of comparison methods on Dataset 1

From: Adaptive deep feature representation learning for cross-subject EEG decoding

Tasks	Comparison Methods
Tasks	EEG_ConvNet	SA	TCA	TJM	DDC	D_CORAL	ADFR
aa/al	0.8964	0.9000	0.9107	0.9179	0.9179	0.9214	0.9393
aa/av	0.5107	0.5250	0.5321	0.5571	0.5679	0.5714	0.6107
aa/aw	0.7929	0.8071	0.8179	0.8321	0.8964	0.9000	0.9143
aa/ay	0.6036	0.6179	0.6393	0.6429	0.7679	0.7750	0.8179
al/aa	0.5893	0.6964	0.7179	0.7250	0.8321	0.8464	0.8643
al/av	0.5500	0.5964	0.6107	0.6071	0.6071	0.6107	0.6393
al/aw	0.7714	0.8393	0.8571	0.8643	0.9143	0.9036	0.9500
al/ay	0.7821	0.8464	0.8607	0.8536	0.8643	0.8714	0.8964
av/aa	0.6321	0.6321	0.6214	0.6393	0.6857	0.6607	0.7000
av/al	0.5964	0.6250	0.6500	0.6607	0.6679	0.6750	0.7071
av/aw	0.5250	0.5286	0.5964	0.6107	0.6857	0.7071	0.7429
av/ay	0.5536	0.5893	0.6321	0.6571	0.6750	0.6679	0.7000
aw/aa	0.6357	0.6393	0.7000	0.7071	0.7179	0.7429	0.7750
aw/al	0.8714	0.8929	0.8893	0.8964	0.8964	0.9071	0.9179
aw/av	0.5286	0.5286	0.5464	0.5500	0.5536	0.5857	0.6321
aw/ay	0.6393	0.7143	0.7786	0.7857	0.7964	0.8143	0.8357
ay/aa	0.5107	0.5143	0.5393	0.5429	0.5464	0.5536	0.5857
ay/al	0.6857	0.7607	0.7821	0.7964	0.8679	0.8893	0.9071
ay/av	0.5071	0.5714	0.5786	0.5857	0.5714	0.5893	0.6179
ay/aw	0.5107	0.5679	0.5714	0.5897	0.6500	0.6750	0.7143
Avg.	0.6346	0.6696	0.6916	0.7011	0.7341	0.7434	0.7734

Bold values indicate statistically significant results (p < 0.05)

Back to article page

ISSN: 1471-2105

Contact us

General enquiries: journalsubmissions@springernature.com