The multi-channel nature of EEG a data poses a big challenge to the development of automatic EEG analysis and classification systems. Due to the “curse of dimensionality” problem, the analysis and classification of several channels may not lead to the desired performance. Accordingly, a number of algorithms have been proposed to identify small ”static” subsets of channels that are capable of differentiating between samples of different classes. However, the identification of small subsets of relevant channels may not always be possible, where for certain applications the smaller the number of channels the less chance that sufficient information is provided. The propose in this project is a dynamic channel selection using GrassbergerProcaccia algorithm that identifies a channel (or a subset of channels) for each time segment of the signal that is relevant to the class of that particular time segment. To achieve this, we embraced the Grassberger–Procaccia algorithm methodology, and particularly the multiple classifier behaviour approaches. Each EEG channel can be chosen to represent a certain unseen time segment of the signal based on the performance, or local accuracy, of its nearest neighbors in the set of training time segments. Results obtained using EEG data from a four-class alertness state classification problem reveal that the proposed approach is capable of achieving competitive performance compared to a traditional static channel selection based method. The algorithm also produced very encouraging results when a method developed by Grassberger Procaccia allows estimation of the dimensional complexity of the state‐space attractor of a time series. Saturation of dimensional‐complexity estimates with increasing values of embedding dimension is considered a strong indication that the time series is governed by deterministic chaos. The present investigation employed the Grassberger‐Procaccia method to estimate EEG dimensional complexity in a multi‐subject, factorial experiment.