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moabb/results/within_session_mi_all_classes.json at develop · allwaysFindFood/moabb · GitHub
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{
"data"
: [
[
"
ACM+TS+SVM
"
,
"
69.37
\u00b1
15.07
"
,
"
77.82
\u00b1
12.23
"
,
"
55.44
\u00b1
14.87
"
,
"
82.50
\u00b1
10.20
"
,
"
63.89
\u00b1
11.01
"
,
"
85.25
\u00b1
4.06
"
],
[
"
CSP+LDA
"
,
"
61.04
\u00b1
17.22
"
,
"
65.99
\u00b1
15.47
"
,
"
47.73
\u00b1
14.35
"
,
"
72.97
\u00b1
10.42
"
,
"
39.45
\u00b1
11.87
"
,
"
82.96
\u00b1
5.20
"
],
[
"
CSP+SVM
"
,
"
62.92
\u00b1
16.89
"
,
"
66.88
\u00b1
15.22
"
,
"
48.52
\u00b1
14.62
"
,
"
75.89
\u00b1
10.55
"
,
"
44.08
\u00b1
11.95
"
,
"
83.08
\u00b1
5.33
"
],
[
"
DLCSPauto+shLDA
"
,
"
60.63
\u00b1
17.91
"
,
"
66.31
\u00b1
15.36
"
,
"
46.85
\u00b1
14.65
"
,
"
72.82
\u00b1
10.44
"
,
"
38.84
\u00b1
11.97
"
,
"
82.06
\u00b1
5.57
"
],
[
"
DeepConvNet
"
,
"
37.71
\u00b1
4.56
"
,
"
35.29
\u00b1
8.26
"
,
"
27.68
\u00b1
3.91
"
,
"
56.78
\u00b1
18.11
"
,
"
24.17
\u00b1
9.80
"
,
"
55.69
\u00b1
5.61
"
],
[
"
EEGITNet
"
,
"
36.04
\u00b1
3.43
"
,
"
35.55
\u00b1
6.35
"
,
"
26.15
\u00b1
4.95
"
,
"
70.44
\u00b1
14.68
"
,
"
25.78
\u00b1
8.00
"
,
"
50.68
\u00b1
16.27
"
],
[
"
EEGNeX
"
,
"
37.71
\u00b1
9.64
"
,
"
45.62
\u00b1
15.29
"
,
"
26.69
\u00b1
5.64
"
,
"
67.56
\u00b1
14.15
"
,
"
30.22
\u00b1
11.02
"
,
"
56.42
\u00b1
11.29
"
],
[
"
EEGNet_8_2
"
,
"
43.96
\u00b1
8.62
"
,
"
60.46
\u00b1
20.20
"
,
"
29.04
\u00b1
7.03
"
,
"
76.99
\u00b1
13.05
"
,
"
35.35
\u00b1
14.05
"
,
"
83.34
\u00b1
3.58
"
],
[
"
EEGTCNet
"
,
"
34.17
\u00b1
1.86
"
,
"
41.65
\u00b1
13.73
"
,
"
25.79
\u00b1
3.85
"
,
"
71.11
\u00b1
11.96
"
,
"
17.95
\u00b1
3.88
"
,
"
37.19
\u00b1
2.57
"
],
[
"
FilterBank+SVM
"
,
"
65.00
\u00b1
17.56
"
,
"
66.53
\u00b1
12.05
"
,
"
45.49
\u00b1
12.54
"
,
"
75.94
\u00b1
8.59
"
,
"
45.21
\u00b1
10.05
"
,
"
81.99
\u00b1
4.65
"
],
[
"
FgMDM
"
,
"
65.63
\u00b1
15.63
"
,
"
70.14
\u00b1
15.13
"
,
"
55.04
\u00b1
14.17
"
,
"
82.97
\u00b1
10.08
"
,
"
56.94
\u00b1
9.26
"
,
"
83.07
\u00b1
4.96
"
],
[
"
MDM
"
,
"
60.62
\u00b1
13.69
"
,
"
61.60
\u00b1
14.20
"
,
"
42.96
\u00b1
12.98
"
,
"
52.03
\u00b1
10.11
"
,
"
33.41
\u00b1
8.67
"
,
"
76.05
\u00b1
7.10
"
],
[
"
ShallowConvNet
"
,
"
50.00
\u00b1
12.94
"
,
"
72.47
\u00b1
16.50
"
,
"
41.87
\u00b1
12.50
"
,
"
85.13
\u00b1
9.57
"
,
"
48.94
\u00b1
10.36
"
,
"
85.02
\u00b1
3.78
"
],
[
"
TS+EL
"
,
"
69.79
\u00b1
13.75
"
,
"
72.38
\u00b1
14.85
"
,
"
59.93
\u00b1
14.07
"
,
"
85.53
\u00b1
9.40
"
,
"
63.84
\u00b1
8.77
"
,
"
84.54
\u00b1
4.93
"
],
[
"
TS+LR
"
,
"
69.17
\u00b1
14.79
"
,
"
71.97
\u00b1
15.46
"
,
"
58.55
\u00b1
14.06
"
,
"
84.60
\u00b1
9.28
"
,
"
62.76
\u00b1
8.39
"
,
"
84.88
\u00b1
4.63
"
],
[
"
TS+SVM
"
,
"
67.92
\u00b1
12.74
"
,
"
70.76
\u00b1
15.08
"
,
"
58.46
\u00b1
15.15
"
,
"
84.41
\u00b1
9.56
"
,
"
61.47
\u00b1
9.62
"
,
"
83.66
\u00b1
4.55
"
]
]
}
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