signalforluster

Signalforluster is a data analysis technique that combines signal processing with clustering to identify coherent patterns in noisy, multivariate time series. The method treats transient or recurring signal motifs as clusters in a feature space derived from the original signals rather than as isolated events. The term is a portmanteau of signal and cluster with an emphasis on recognizing motif-like structures across time and channels.

The approach typically involves two main stages. First, feature extraction computes representations such as instantaneous amplitude and phase via analytic signals (Hilbert transform or wavelets), local frequency, energy, cross-channel coherence, and statistical descriptors over sliding windows. These features produce a high-dimensional representation that captures temporal and spectral characteristics. Second, a clustering step groups time windows or short segments using a metric that reflects temporal similarity and cross-channel relationships. The number of clusters can be chosen by standard model selection criteria or determined in a data-driven manner. The output is a labeled partition of the data into motif-like clusters, possibly with ambiguity for overlapping segments.

Applications of signalforluster span several fields. In neuroscience, it has been used for EEG or MEG event discovery; in seismology, to detect microseismic events; in audio and speech processing, for event detection; and in industrial sensing, to identify anomalous operating states.

Limitations of the method include sensitivity to preprocessing choices, feature design, and clustering parameters; computational cost can be high for large datasets; and interpretation often requires domain expertise.

See also: clustering, time-series analysis, motif discovery, spectral clustering, signal processing.