Hyperscanning research can be informed by intra- and inter-brain analysis. Intra-brain analysis looks at the individual participant and quantifies, for example, the ERP, spectral power, or connectivity. Inter-brain analysis is interesting because it measures whether brains of different participants engage in similar processes during social interaction. This is often quantified with connectivity methods that are also applied for intra-brain analysis.
Similarity between brain activity of two participants is measured by correlating the signal amplitudes, phases, or both. Which approach is more suitable depends on the requirements of the research question. Amplitude based measures include the correlation of power or envelope. You can obtain them in Analyzer with the FFT, Complex Demodulation and Correlation Measures transformations. The Complex Demodulation can be understood as the analytical signal comparable to the Hilbert transform.
More common, though, are Fourier spectrum or Wavelet-based metrics. For example, the Phase Locking Value (PLV) estimates phase-synchrony while the Magnitude-Squared Coherence calculates similarity based on phase and amplitude. These measures are available in Analyzer via the FFT, Wavelet, Coherence or Correlation Measures transformations. Check out our webinar recording on Phase- and Connectivity Analysis and our newsletter article including a description for implementing the PLV. Additional methods for quantifying correlation and other comparisons are offered in the Cross-Correlation and Data Comparison transformations.
Analyzer has dedicated Channel Pair views for displaying relations between channels. They are designed for single heads and show connectivity between channels of the same participant. If you are comparing across channels and participants, this display might make sense to some degree. It does not make sense, though, if you are comparing the same channels between participants. Instead, we recommend displaying the frequency spectrum or time (-frequency) domain signals in the Grid View.
Pairwise correlations as in the above-mentioned connectivity modules can get quite complex. This happens, in particular, if pairs are formed between multiple channels of more than two subjects. Entering or editing many channel pairs in the Manual Channel Pair Selection is quite cumbersome. For correlating symmetric pairs between two participants with conventional 10-20 labels and channel suffixes (as described above), we have created a BrainVision Graph file for your convenience. You can load it in the Manual Channel Pair Selection wizard via Load Graph in transformations dealing with channel pairs.
We recommend, however, to consider reducing complexity for computational reasons as well. You can, for example, choose a subset of representative channels to perform pairwise correlations.
Once you are happy with your analysis, you can export data for statistical evaluation. If you have been looking at time-domain or frequency domain data, you can use the Area Information or Peak Information Export modules. If you have calculated time-frequency domain metrics, we recommend using the Wavelet Data Export Solution allowing you to aggregate and export data from a time and frequency range.
Irrespective of how you have recorded your data, we hope the tools above help to pursue your hyperscanning analysis as you desire.
History Files in Analyzer can get quite large if the dataset is large. Sometimes this leads to memory issues. If your paradigm contains many participants or is rather long, we recommend considering having participants in individual but synchronized Raw Data files. Reducing the size of data before merging, for example by segmentation, can help managing memory bottlenecks.
While having all participants in one dataset is necessary for correlation, for other processing steps you might benefit from handling one dataset at a time. For example, when screening for artifacts. In other situations, the method might even be invalidated if all participants are in one dataset. For example, if you want to use the Mapping View or compute source activity with the LORETA transformation. It makes sense to plan out your analysis thoroughly before starting to record such a complex dataset.
Make sure to read along your peer literature to understand the pitfalls accompanying hyperscanning connectivity findings. One caveat to consider is whether the modulation you observe relates to the interactive aspects of your paradigm or to a common external stimulus. The good news is that other caveats such as volume conduction or the common reference problem are not issues for inter-brain connectivity.
As usual we are more than happy to help you in every step of your analysis. Please do not hesitate to write us an email via email@example.com.