Text 1 — Electroencephalography

The electroencephalogram (EEG) is a powerful tool that employs electrodes to record voltage changes in cortical neuronal clusters situated on the intact surface of the skull. This technique is crucial for monitoring brain activity in both clinical and research settings. Brain electrical activity can be divided into two main categories: background or spontaneous activity and evoked activity, the latter of which can be recorded using event-related potentials (ERPs). EEG background activity consists of oscillations at various frequencies, which are analyzed and categorized into distinct frequency ranges or frequency bands through spectral analysis. The ERP is particularly significant as it reflects the electrocortical activity of the brain that is triggered by a physical stimulus and is influenced by various psychological processes such as attention, memory, and other cognitive factors. Since the ERP is directly temporally related to the triggering physical stimulus, researchers can effectively separate the ERP from concurrent cortical spontaneous activity by averaging several repeated stimulus presentations. This averaging technique allows for the spontaneous activity that is not temporally related to the physical stimulus being processed to be effectively filtered out, while simultaneously enhancing the clarity and prominence of the electrophysiological response to the stimulus. Individual ERP components are classified based on their electrical polarity (positive or negative) and their time of occurrence (early or late components). Early components, which occur shortly after the presentation of a sensory stimulus, typically appear within less than 200 milliseconds (ms). When analyzing ERP components, researchers focus on differences in latency and amplitude. Latency differences pertain to the timing of when a component appears after stimulus presentation, while amplitude reflects the strength of the electrophysiological signal measured in microvolts. The spatial distribution of potentials on the skull surface also provides valuable information about the specific brain regions that exhibit the highest levels of activity in response to various stimuli. One notable early negative component, which peaks approximately 100 ms after the onset of a stimulus presentation, is known as the N100. The N100 component is subject to variation based on the physical properties of the stimulus, such as stimulus intensity, and it is also linked to attentional processes. Given that the expression of this early component is primarily influenced by the physical characteristics of the presented sensory stimulus, it is often classified as an exogenous component, highlighting its reactive nature to external stimuli. On the other hand, late ERP components arise when a subject actively engages in a task that requires attention, discrimination, naming, recognition, classification, or evaluation. Consequently, these ERP components are categorized as cognitive or endogenous components, indicating their involvement in higher-order cognitive functions. A prominent example of such an endogenous positive ERP component is the P300, which can be observed in contexts such as simple decision-making tasks. Generally, the latency of the P300 is interpreted as an indicator of the time required for the cognitive evaluation of a stimulus. It is worth noting that similar to reaction time, P300 latency tends to increase with greater task complexity, while P300 amplitude typically decreases as task difficulty escalates. Therefore, both P300 latencies and P300 amplitudes serve as informative measures for understanding the cognitive resources necessary to solve the task at hand. In summary, the clear advantages of ERPs compared to imaging methods lie in their excellent temporal resolution within the millisecond range, which allows for precise tracking of brain activity as it unfolds in real time. However, it is important to acknowledge that the spatial resolution of ERPs is considerably poorer compared to imaging methods. This limitation arises from the presence of various anatomical structures, such as the meninges and skull bones, which exhibit differing electrical conductivity between the current source (i.e., the neuron clusters) and the recording site. Despite this limitation, the utility of EEG and ERPs in capturing dynamic brain processes remains invaluable in both clinical and experimental contexts.

The following visualization shows the distribution of different frequency bands across diverse brain states, measured by an EEG.