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<h1 id="neuraspeech">NeuraSpeech</h1>
<p>NeuraSpeech is a Python toolbox for analyzing EEG responses during continuous speech listening. It includes utilities for <strong>phoneme-related potential (PRP) analysis</strong> and <strong>temporal response function (TRF) modeling</strong>.</p>
<p><em>The package is currently private and under development. Please contact Zhe-chen Guo (zcguo@northwestern.edu) for access or quesitons.</em></p>
<h2 id="features">Features</h2>
<ul>
<li>PRP dataset handling</li>
<li>Topographic and time-course visualization</li>
<li>Phoneme manner separability analyses (F-statistic, machine-learning classification, etc.)</li>
<li>TRF estimation with support for parallel processing</li>
</ul>
<h2 id="whats-new">What's new?</h2>
<p>07/12/2026:
<strong>[Version 0.2.9]</strong> Added <code>make_predictors_from_tables()</code> to <code>trf</code> module to allow generation of impulse-like predictors using tables or spreadsheets. See the <em>Predictors from tables</em> tab for usage.</p>
<p>07/10/2026:
<strong>[Version 0.2.8]</strong> Added <code>make_pitch_predictors()</code> to <code>trf</code> module to extract pitch contours and generate pitch predictors. This new functionality is implemented using <a href="https://pypi.org/project/praat-parselmouth/">praat-parselmouth</a> so you would need to install it or update your neuraspeech environment using the updated <code>environment.yml</code>. See the <em>Pitch predictors</em> tab for usage.</p>
<p>06/29/2026:
<strong>[Version 0.2.7]</strong> Added <code>make_predictors_from_textgrids()</code> to <code>trf</code> module to allow predictor generation using TextGrid annotations of the audio stimuli.</p>
<p><em>See more in <a href="history/">Update history</a>.</em></p>
<h2 id="quick-example">Quick example</h2>
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">neuraspeech</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ns</span>
<span class="c1"># Load data</span>
<span class="n">path_to_data</span> <span class="o">=</span> <span class="s1">'mydata'</span>
<span class="n">path_to_montage</span> <span class="o">=</span> <span class="s1">'my_montage.locs'</span>
<span class="n">prps</span> <span class="o">=</span> <span class="n">ns</span><span class="o">.</span><span class="n">PRPData</span><span class="p">(</span><span class="n">path_to_data</span><span class="p">,</span> <span class="n">montage_path</span> <span class="o">=</span> <span class="n">path_to_montage</span><span class="p">)</span>
<span class="n">ns</span><span class="o">.</span><span class="n">plot_manner_prps</span><span class="p">(</span>
<span class="n">split_by</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'group'</span><span class="p">],</span> <span class="n">line_by</span> <span class="o">=</span> <span class="s1">'manner'</span><span class="p">,</span>
<span class="n">equal_y_scale</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">lw</span> <span class="o">=</span> <span class="mf">3.0</span><span class="p">)</span>
</code></pre></div>
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