Neuron-Level Architecture Growth: A Controlled Evaluation for EEG Time-Series Decoding
Authors: Adam Mounir, Stella Douka, Arnault H. Caillet, Bruno Aristimunha, Sylvain Chevallier
Organizations: Inria TAU – LISN, Université Paris-Saclay, France · Yneuro, Paris, France · Imperial College London, UK · University of California San Diego, CA, USA
Convolutional EEG decoders are trained at a fixed width, usually set by their authors on other data. Growing methods add neurons during training where the loss could decrease the most, but whether they improve compared to a reference width is untested on EEG. Here, we grow three convolutional backbones on 12 motor-imagery datasets under three protocols and compare each with its reference model per subject. The growing ShallowFBCSPNet scores 2.9 points above its reference model with only half the parameters (0.57x), SCCNet changes by at most 1.2 points. Deep4Net growing models show decreased accuracy, but they require adaptation that prevent to compare faithfully the results. These differences follow the selection step, which keeps a candidate neuron relying on a dynamic threshold from singular values decomposition. Overall, these results suggest that growth helps when its criterion can rank the candidate neurons, and that the rate of skipped neuron addition tells where a decoder can be grown small from scratch.
Figures & tables
Figure 1: (a) The desired update vgoal splits into a reachable part and a residual, the expressivity bottleneck, which growth covers. (b) On EEG, both read as scalp patterns (schematic).
Backbone
Growable layer
Width
Reference
Shallow
temporal → spatial
8→40
ShallowFBCSPNet, 40
SCCNet
spatial → spatio-temp.
4→22
SCCNet, 22
Deep
conv2a → conv2b
8→32
Deep4Net, 4 stages
Table 1: Backbones, the layer that grows and its width range. Only the first two rows are width-matched.
Protocol
Net
Δgrow [95 % CI]
MDE
ahead
Within-sess.
Shallow
+2.9
[+2.3,+3.5]
0.9
240/324
Deep
−4.0
[−4.9,−3.1]
1.3
107/324
SCCNet
+0.4†
[−0.2,+0.9]
0.8
172/324
Cross-sess.
Shallow
+2.8
[+1.9,+3.7]
1.3
85/116
Deep
−3.6
[−5.1,−2.2]
2.0
28/116
SCCNet
+1.2
[+0.4,+2.1]
1.2
72/116
Table 2: Growth is ahead for Shallow and behind for Deep in every protocol. Δgrow : growing minus reference score, in points. Brackets: 95 % bootstrap intervals over subjects. Ahead : subjects on which the growing backbone scores higher. MDE: minimum detectable effect at 80 % power. † marks the two cells whose effect falls below it.