Papers for

neural signal processing engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Intracortical motor decoder adapts across sessions with fixed network weights

Association profile conditioning in a set-temporal transformer for cross-session intracortical motor decoding

Abstract: Intracortical motor decoders degrade across sessions because the set of recorded units changes and persisting units can alter how their firing relates to behavior. Most existing methods update network weights on each new session or rely on unlabeled activity, which does not directly reveal such changes. We present APST, an Association Profile-conditioned Set-Temporal transformer that adapts to new sessions with all network weights frozen. From a few labeled calibration trials, APST summarizes how each unit's firing relates to behavior in a four-dimensional association profile computed in closed form. The profiles condition a set-attention encoder that accepts any number and order of units, followed by a causal transformer for streaming decoding. On held-out DANDI688 sessions from two monkeys, APST reaches velocity $R^2$ of $0.78$ and $0.81$, versus $0.40$ and $0.58$ for a variant that uses neural activity alone, and matches or exceeds an RNN fine-tuned on the same trials. On FALCON private held-out evaluation, it attains $R^2$ of $0.65$, $0.42$, and $0.44$ on M1, M2, and H1.

Wed 30 SeptArtificial Intelligence

The gist

Intracortical devices that decode brain signals to control movements usually lose accuracy over time because the neurons they record from change or behave differently. The authors present a method called APST that adjusts to these changes without retraining the whole model, by analyzing how each neuron’s activity relates to movement during a few short calibration periods. This approach uses a transformer model that can handle varying numbers and orders of neurons and improves accuracy in predicting movement velocity across multiple sessions. APST outperforms methods that rely on neural data alone or require fine-tuning with the same calibration data.

Open → 2609.39080v1

Neuron level growth improves some EEG convolutional decoders accuracy

Neuron-Level Architecture Growth: A Controlled Evaluation for EEG Time-Series Decoding

Abstract: 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.

Sun 27 SeptHuman-Computer InteractionMachine LearningNeural and Evolutionary Computing

The gist

Many EEG decoding models are built with a fixed number of neurons, which might not be the best size for every dataset. The authors tested methods that add neurons during training only where it helps the model improve the most. They found that for some simpler brain activity decoders, growing the network helped improve accuracy and reduced the number of parameters needed. However, for deeper models, growing neurons did not improve performance as expected. This suggests that carefully deciding where to add neurons matters for improving EEG decoding.

Open → 2609.33880v1