These sensors detect and measure the electrical potentials or magnetic fields generated by neural activity in the brain. Each sensor captures the neural signals from a specific location on the scalp or above the head.
The main objective of SSA is to analyze and interpret the spatiotemporal patterns of neural activity recorded by these sensors. It allows researchers to investigate various aspects of brain function, such as perceptual processing, attention, memory, language, and motor control. SSA provides insights into how different brain regions and networks contribute to specific cognitive processes.
The general steps involved in sensor space analysis include:
Preprocessing: The recorded EEG or MEG data undergoes preprocessing steps, which may include filtering, artifact removal (e.g., eye blinks, muscle artifacts), and baseline correction.
Time-Frequency Analysis: Time-frequency analysis is often applied to extract oscillatory activity in different frequency bands over time. This analysis provides information about the power or amplitude of neural oscillations at different time points and frequencies.
Sensor-level Analysis: activity recorded by individual sensors is analyzed. Various statistical techniques, such as event-related potential (ERP) analysis or time-frequency analysis, are used to examine sensor-level responses to experimental manipulations or cognitive tasks. This analysis focuses on the amplitude, latency, or spectral characteristics of neural responses at specific sensors.
Statistical Inference: Statistical tests are performed to determine the significance of observed effects or differences in neural activity across experimental conditions or groups. This step involves comparing sensor-level responses using appropriate statistical tests, such as t-tests, analysis of variance (ANOVA), or non-parametric tests.
Visualization: The results of the sensor-level analysis can be visualized using topographic maps, which illustrate the spatial distribution of neural activity across sensors. These maps help identify regions of interest and reveal the scalp distribution of neural responses.
SSA provides a detailed understanding of the dynamics of neural activity at the sensor level, allowing researchers to investigate the temporal and spatial characteristics of cognitive processes. It serves as a foundation for subsequent source-level analysis, which aims to localize the neural sources contributing to the observed sensor-level responses. By combining sensor and source space analyses, researchers can gain comprehensive insights into brain function and connectivity during various cognitive tasks or experimental manipulations
The main objective of SSA is to analyze and interpret the spatiotemporal patterns of neural activity recorded by these sensors. It allows researchers to investigate various aspects of brain function, such as perceptual processing, attention, memory, language, and motor control. SSA provides insights into how different brain regions and networks contribute to specific cognitive processes.
The general steps involved in sensor space analysis include:
Preprocessing: The recorded EEG or MEG data undergoes preprocessing steps, which may include filtering, artifact removal (e.g., eye blinks, muscle artifacts), and baseline correction.
Time-Frequency Analysis: Time-frequency analysis is often applied to extract oscillatory activity in different frequency bands over time. This analysis provides information about the power or amplitude of neural oscillations at different time points and frequencies.
Sensor-level Analysis: activity recorded by individual sensors is analyzed. Various statistical techniques, such as event-related potential (ERP) analysis or time-frequency analysis, are used to examine sensor-level responses to experimental manipulations or cognitive tasks. This analysis focuses on the amplitude, latency, or spectral characteristics of neural responses at specific sensors.
Statistical Inference: Statistical tests are performed to determine the significance of observed effects or differences in neural activity across experimental conditions or groups. This step involves comparing sensor-level responses using appropriate statistical tests, such as t-tests, analysis of variance (ANOVA), or non-parametric tests.
Visualization: The results of the sensor-level analysis can be visualized using topographic maps, which illustrate the spatial distribution of neural activity across sensors. These maps help identify regions of interest and reveal the scalp distribution of neural responses.
SSA provides a detailed understanding of the dynamics of neural activity at the sensor level, allowing researchers to investigate the temporal and spatial characteristics of cognitive processes. It serves as a foundation for subsequent source-level analysis, which aims to localize the neural sources contributing to the observed sensor-level responses. By combining sensor and source space analyses, researchers can gain comprehensive insights into brain function and connectivity during various cognitive tasks or experimental manipulations
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