Perturbational Complexity Index (PCI) is a quantitative measure used in neuroscience to assess the level of consciousness based on the complexity of brain responses to external perturbations, typically induced via transcranial magnetic stimulation (TMS). It was introduced in 2013 by Italian M.D.–Ph.D. Marcello Massimini and colleagues as a practical application of principles from Integrated Information Theory (IIT), which posits that conscious systems must exhibit both high integration and differentiation of information.

PCI quantifies the algorithmic complexity of the brain’s response to a controlled perturbation. In a typical protocol, a combined TMS-EEG paradigm is used. A brief TMS pulse is delivered to the cortex, and the resulting electrical activity is recorded with electroencephalography (EEG). The recorded spatiotemporal EEG response is then binarized and compressed using a lossless algorithm to estimate its algorithmic complexity. The PCI value is normalized to control for signal length and amplitude.

Formally, PCI is defined as the normalized Lempel–Ziv complexity of the binarized EEG response:

where, ${\displaystyle S}$ is the spatiotemporal matrix of EEG responses to TMS, ${\displaystyle C(S)}$ denotes its Lempel–Ziv complexity, and ${\displaystyle N}$ is a normalization constant (typically sequence length). This formalization captures both the integration (distributed activation) and differentiation (structured diversity) of cortical responses.

Higher PCI values correspond to rich and differentiated responses, suggesting conscious states. Lower PCI values reflect stereotyped or globally synchronized responses, typically associated with unconscious states like deep sleep, general anesthesia, or coma.

PCI has been used to objectively differentiate among various states of consciousness, including:

Its most notable clinical use is in the diagnosis and prognosis of disorders of consciousness (DoC), where behavioral assessments may be unreliable.

Although PCI was inspired by IIT, it is not a direct measure of IIT’s formal quantity Φ (phi). Rather, it is considered a proxy that empirically captures the principles of information integration and differentiation using experimentally accessible brain data.