Example 6: How to plot the phase

Except the SNR calculated in the 5th example, the phase synchronization is another important characteristic that affects the recognition performance. This example shows how to use the build-in function to calculate phases of the SSVEP signals.

You can find the related code in demo/plot_phase.py or demo/plot_phase.ipynb.

The Benchmark Dataset is applied as an example. Firstly, we need to initialize the dataset.

from SSVEPAnalysisToolbox.datasets import BenchmarkDataset
from SSVEPAnalysisToolbox.utils.benchmarkpreprocess import (
    preprocess, filterbank
)
dataset = BenchmarkDataset(path = '2016_Tsinghua_SSVEP_database')
dataset.regist_preprocess(preprocess)
dataset.regist_filterbank(filterbank)

This example uses first 1 second signal length and only consider 9 occipital channel to compute the phases.

sig_len = 1
from SSVEPAnalysisToolbox.utils.algsupport import nextpow2
from SSVEPAnalysisToolbox.utils.benchmarkpreprocess import suggested_ch
phase = dataset.get_phase(display_progress = True,
                      sig_len = sig_len,
                      remove_break = False, remove_pre_and_latency = False, remove_target_phase = True,
                      NFFT = 2 ** nextpow2(10*dataset.srate)) # filterbank index is 0
phase_list = phase[:,:,:,suggested_ch()]

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Then, we can plot the phase distribution.

from SSVEPAnalysisToolbox.evaluator import (
    polar_phase_shadow, gen_colors
)
color = gen_colors(1)
fig, ax = polar_phase_shadow(phase_list,
                            color = color,
                            grid = True,
                            errorbar_type = 'std')
fig.savefig('res/phase_benchmark.jpg', bbox_inches='tight', dpi=300)