It is well established that multiple EEG channels are required for various brain functionality studies, including classification tasks. Yet, due to the curse of dimensionality problem, the analysis of multiple channels may not lead to the desired performance. Accordingly, a number of static channel selection algorithms have been proposed to identify the most relevant subset of channels. However, static methods select a fixed subset of channels that is unchanged when processing new data, and hence cannot adapt to changes in data. In this paper, we propose a novel algorithm that utilizes the dynamic classification behaviour of channels in selecting the channel that is most relevant for each time segment of the signal. The main idea is to identify for each time segment of every channel of the signal (testing sample) the closest training samples. These training samples are used to estimate the local accuracy of each channel. The best performing channel for that time segment will then be identified as the relevant one. Results obtained using EEG data of a four-class alertness state classification problem, with two different feature sets, reveal that the proposed approach is capable of achieving competitive performance compared to a traditional static channel selection based method. More importantly, the evaluation of the selected channels reveals that our approach is able to select relevant channels for each of the four alertness states. The proposed algorithm is expected to make a valuable contribution to the field of multichannel biomedical signal classification.