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EEGNet:
EEGNetWrapper(
(eegnet): EEGNet(
(block1): Sequential(
(0): Conv2d(1, 8, kernel_size=(1, 64), stride=(1, 1), padding=(0, 32), bias=False)
(1): BatchNorm2d(8, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): Conv2dWithConstraint(8, 16, kernel_size=(16, 1), stride=(1, 1), groups=8, bias=False)
(3): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(4): ELU(alpha=1.0)
(5): AvgPool2d(kernel_size=(1, 4), stride=4, padding=0)
(6): Dropout(p=0.5, inplace=False)
)
(block2): Sequential(
(0): Conv2d(16, 16, kernel_size=(1, 16), stride=(1, 1), padding=(0, 8), groups=16, bias=False)
(1): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(3): ELU(alpha=1.0)
(4): AvgPool2d(kernel_size=(1, 8), stride=8, padding=0)
(5): Dropout(p=0.5, inplace=False)
)
(lin): Linear(in_features=64, out_features=5, bias=False)
)
)
======================================================================
Layer (type:depth-idx) Param #
======================================================================
├─EEGNet: 1-1 --
| └─Sequential: 2-1 --
| | └─Conv2d: 3-1 512
| | └─BatchNorm2d: 3-2 16
| | └─Conv2dWithConstraint: 3-3 256
| | └─BatchNorm2d: 3-4 32
| | └─ELU: 3-5 --
| | └─AvgPool2d: 3-6 --
| | └─Dropout: 3-7 --
| └─Sequential: 2-2 --
| | └─Conv2d: 3-8 256
| | └─Conv2d: 3-9 256
| | └─BatchNorm2d: 3-10 32
| | └─ELU: 3-11 --
| | └─AvgPool2d: 3-12 --
| | └─Dropout: 3-13 --
| └─Linear: 2-3 320
======================================================================
Total params: 1,680
Trainable params: 1,680
Non-trainable params: 0
1 - EEGNet - Normal split
S06S01
EEG Data Shape
-
- 2
(153,586, 2000)
Labels Shape
-
-
(153,586,)
Size
122,868
30,718
153,586
forward
51,687
19,825
-
reverse
26,941
-
-
stop
38,828
8,928
-
turn_left
2,838
1,664
-
turn_right
2,574
301
-
Average Loss
1.1150
-
-
Highest Train Accuracy
48.71%
-
-
Test Accuracy
-
36.50%
-
There are no rows in this table
S06S06
EEG Data Shape
-
- 2
(91,163, 2000)
Labels Shape
-
-
(91,163,)
Size
72,930
18,233
91,163
forward
29,772
10,195
-
reverse
14,180
-
-
stop
27,465
7,354
-
turn_left
168
466
-
turn_right
1,345
218
-
Average Loss
0.8834
-
-
Highest Train Accuracy
60.92%
-
-
Test Accuracy
-
47.25%
-
There are no rows in this table
S06S07
EEG Data Shape
-
- 2
(91,111, 2000)
Labels Shape
-
-
(91,111,)
Size
72,888
18,223
91,111
forward
27,183
9,464
-
reverse
13,248
-
-
stop
31,487
8,212
-
turn_left
143
404
-
turn_right
827
143
-
Average Loss
0.4994
-
-
Highest Train Accuracy
79.98%
-
-
Test Accuracy
-
47.55%
-
There are no rows in this table
S06S08
Metric/Label
-
- 2
(153,586, 2000)
EEG Data Shape
-
-
(153,586, 2000)
Labels Shape
-
-
(153,586,)
Size
122,868
30,718
153,586
forward
51,687
19,825
-
reverse
26,941
-
-
stop
38,828
8,928
-
turn_left
2,838
1,664
-
turn_right
2,574
301
-
Average Loss
1.1150
-
-
Highest Train Accuracy
48.71%
-
-
Test Accuracy
-
36.50%
-
There are no rows in this table
S06S09
EEG Data Shape
-
- 2
(87,944, 2000)
Labels Shape
-
-
(87,944,)
Size
70,355
17,589
87,944
forward
26,354
9,744
-
reverse
13,365
-
-
stop
29,725
7,341
-
turn_left
173
346
-
turn_right
738
158
-
Average Loss
0.8630
-
-
Highest Train Accuracy
61.64%
-
-
Test Accuracy
-
36.49%
-
There are no rows in this table
S06S10
Metric/Label
Training Set
Testing Set
Overall
EEG Data Shape
-
-
(72,972, 2000)
Labels Shape
-
-
(72,972,)
Size
58,377
14,595
72,972
forward
25,227
-
-
reverse
4,554
9,124
-
stop
27,724
5,471
-
turn_left
247
-
-
turn_right
625
-
-
Average Loss
0.7739
-
-
Highest Train Accuracy
61.70%
-
-
Test Accuracy
-
47.34%
-
There are no rows in this table
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