Correct TRCA (NeuroTechX#522)

* fix: try to fix TRCA

* fix: remove n_fbands as not used in filterbanks

* fix: adapt filter

* fix: correct ssvep example

* enh: add whats new

* enh: adding a check for update of whats new file

* fix: correct filepath

---------

Co-authored-by: Sylvain Chevallier <sylain.chevallier@universite-paris-saclay.fr>

.github/workflows/whats-new.yml

@@ -0,0 +1,38 @@
+name: Check What's new update
+
+on:
+  push:
+    branches: [develop]
+  pull_request:
+    branches: [develop]
+
+jobs:
+  check-whats-news:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Check for file changes in PR
+        run: |
+          pr_number=${{ github.event.pull_request.number }}
+          response=$(curl -s -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \
+            "https://api.github.com/repos/${{ github.repository }}/pulls/${pr_number}/files")
+
+          file_changed=false
+          file_to_check="docs/source/whats_new.rst" # Specify the path to your file
+
+          for file in $(echo "${response}" | jq -r '.[] | .filename'); do
+            if [ "$file" == "$file_to_check" ]; then
+              file_changed=true
+              break
+            fi
+          done
+
+          if $file_changed; then
+            echo "File ${file_to_check} has been changed in the PR."
+          else
+            echo "File ${file_to_check} has not been changed in the PR."
+            echo "::error::File ${file_to_check} has not been changed in the PR."
+            exit 1
+          fi
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # This token is provided by Actions, you do not need to create your own token

docs/source/whats_new.rst

@@ -23,7 +23,7 @@ Enhancements
 Bugs
 ~~~~
 
-- None
+- Fix TRCA implementation for different stimulation freqs and for signal filtering (:gh:522 by `Sylvain Chevallier`_)
 
 API changes
 ~~~~~~~~~~~

examples/plot_cross_subject_ssvep.py

@@ -104,9 +104,7 @@
 pipelines["CCA"] = make_pipeline(SSVEP_CCA(interval=interval, freqs=freqs, n_harmonics=2))
 
 pipelines_TRCA = {}
-pipelines_TRCA["TRCA"] = make_pipeline(
-    SSVEP_TRCA(interval=interval, freqs=freqs, n_fbands=5)
-)
+pipelines_TRCA["TRCA"] = make_pipeline(SSVEP_TRCA(interval=interval, freqs=freqs))
 
 pipelines_MSET_CCA = {}
 pipelines_MSET_CCA["MSET_CCA"] = make_pipeline(SSVEP_MsetCCA(freqs=freqs))

moabb/pipelines/classification.py

@@ -114,9 +114,6 @@ class SSVEP_TRCA(BaseEstimator, ClassifierMixin):
         Frequencies corresponding to the SSVEP components. These are
         necessary to design the filterbank bands.
 
-    n_fbands : int, default=5
-        Number of sub-bands considered for filterbank analysis.
-
     downsample: int, default=1
         Factor by which downsample the data. A downsample value of N will result
         on a sampling frequency of (sfreq // N) by taking one sample every N of
@@ -188,15 +185,14 @@ def __init__(
         self,
         interval,
         freqs,
-        n_fbands=5,
         downsample=1,
         is_ensemble=True,
         method="original",
         estimator="scm",
     ):
         self.freqs = freqs
         self.peaks = np.array([float(f) for f in freqs.keys()])
-        self.n_fbands = n_fbands
+        self.n_fbands = len(self.peaks)
         self.downsample = downsample
         self.interval = interval
         self.slen = interval[1] - interval[0]

moabb/pipelines/utils.py

@@ -289,6 +289,10 @@ def filterbank(X, sfreq, idx_fb, peaks):
     Code based on the Matlab implementation from authors of [1]_
     (https://github.com/mnakanishi/TRCA-SSVEP).
     """
+    if idx_fb > len(peaks):
+        raise (
+            ValueError("idx_fb should be less than number of SSVEP stimulus frequency")
+        )
 
     # Calibration data comes in batches of trials
     if X.ndim == 3:
@@ -299,39 +303,33 @@ def filterbank(X, sfreq, idx_fb, peaks):
     elif X.ndim == 2:
         num_chans = X.shape[0]
         num_trials = 1
+    else:
+        print("error")
 
     sfreq = sfreq / 2
 
-    min_freq = np.min(peaks)
+    peaks = np.sort(peaks)
     max_freq = np.max(peaks)
 
     if max_freq < 40:
-        top = 100
+        top = 40
     else:
-        top = 115
+        top = 60
     # Check for Nyquist
     if top >= sfreq:
         top = sfreq - 10
 
-    diff = max_freq - min_freq
     # Lowcut frequencies for the pass band (depends on the frequencies of SSVEP)
     # No more than 3dB loss in the passband
-
-    passband = [min_freq - 2 + x * diff for x in range(7)]
+    passband = [peaks[i] - 1 for i in range(len(peaks))]
 
     # At least 40db attenuation in the stopband
-    if min_freq - 4 > 0:
-        stopband = [
-            min_freq - 4 + x * (diff - 2) if x < 3 else min_freq - 4 + x * diff
-            for x in range(7)
-        ]
-    else:
-        stopband = [2 + x * (diff - 2) if x < 3 else 2 + x * diff for x in range(7)]
+    stopband = [peaks[i] - 2 for i in range(len(peaks))]
 
     Wp = [passband[idx_fb] / sfreq, top / sfreq]
-    Ws = [stopband[idx_fb] / sfreq, (top + 7) / sfreq]
+    Ws = [stopband[idx_fb] / sfreq, (top + 20) / sfreq]
 
-    N, Wn = scp.cheb1ord(Wp, Ws, 3, 40)  # Chebyshev type I filter order selection.
+    N, Wn = scp.cheb1ord(Wp, Ws, 3, 15)  # Chebyshev type I filter order selection.
 
     B, A = scp.cheby1(N, 0.5, Wn, btype="bandpass")  # Chebyshev type I filter design