Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-Art and Challenges

Abstract

In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data.

Keywords: brain-machine interface; local field potentials; neuroengineering; neuronal activity; neuronal probes; neuronal signal; neuronal signal processing and analysis; neuronal spikes.

Figure 1

Spatiotemporal range of neurophysiological signal acquisition techniques. Spatiotemporal range of the main techniques to measure neurophysiological signals from the brain. EEG, electroencephalography; MEG, magnetoencephalography.