From f945db3563d2bc3ecf39ed4a77a2ccbe0c3ccede Mon Sep 17 00:00:00 2001
From: ofplarsen <ofplarsen@gmail.com>
Date: Thu, 27 Apr 2023 10:27:46 +0200
Subject: [PATCH] test this delay thing
---
jitter/ BCISpeller/BCISpellerV3.py | 24 ++++++++++++++----------
1 file changed, 14 insertions(+), 10 deletions(-)
diff --git a/jitter/ BCISpeller/BCISpellerV3.py b/jitter/ BCISpeller/BCISpellerV3.py
index 2952113..ac970b1 100644
--- a/jitter/ BCISpeller/BCISpellerV3.py
+++ b/jitter/ BCISpeller/BCISpellerV3.py
@@ -191,9 +191,9 @@ inlet = StreamInlet(streams_counter[0]) #LSL Eyetracker data
inlet_2 = StreamInlet(streams_eeg[0])# LSL EEG data
fs = 250 # Sampling frequency
-delay = 0.061 #Occular delay
-
-fragment_duration = 4+delay # Fragment duration in seconds
+sync_delay = 0.061 #sync delay
+ocular_delay = 0.100 # Ocular delay
+fragment_duration = 4 + sync_delay + ocular_delay # Fragment duration in seconds
fragment_samples = round(fs * fragment_duration)
@@ -218,8 +218,9 @@ while True:
# If buffer is filled with data ready to be compared in CCA, and the start of the buffer is the start of
# the Eye Tracking data (Eye Tracking trigger)
- if (len(buffer) == fragment_samples) and (buffer[0][0] == 1):
- if(buffer[fragment_samples - round(delay * fs)-1][0] != (fragment_samples-round(delay * fs))):
+ if (len(buffer) == fragment_samples) and buffer[0][round(ocular_delay*fs)] == 1 and buffer[0][fragment_samples-round(sync_delay * fs)] != 0:
+ # This prob not working :P
+ if(buffer[fragment_samples - round(sync_delay * fs)-1][0] != (fragment_samples-round(sync_delay * fs))):
print("Found invalid stare")
continue
@@ -250,9 +251,12 @@ while True:
print("--- Filter time: %s seconds ---" % (time.time() - start_time))
print(df['N'].tolist())
+ df['N'] = df['N'].shift(-round(ocular_delay * fs))
+
+
# If any delay added, shift signal accordingly
- df['N'] = df['N'].shift(round(delay*fs))
- df = df.iloc[round(delay*fs):]
+ df['N'] = df['N'].shift(round(sync_delay * fs))
+ df = df.iloc[round(sync_delay * fs):]
# Reset the index
df = df.reset_index(drop=True)
@@ -276,9 +280,9 @@ while True:
X_c, Y_c = ca.transform(X, Y)
# Uses two coefficients pk = sqrt(p1**2+p2*'2)
p1 = np.corrcoef(X_c[:, 0], Y_c[:, 0])[0][1]
- #freqs.append(p1)
- p2 = np.corrcoef(X_c[:, 1], Y_c[:, 1])[0][1]
- freqs.append(np.sqrt(p1 ** 2 + p2 ** 2))
+ freqs.append(p1)
+ #p2 = np.corrcoef(X_c[:, 1], Y_c[:, 1])[0][1]
+ #freqs.append(np.sqrt(p1 ** 2 + p2 ** 2))
cca = freqs
#print("CCA single: " + str(perform_cca(df,1)))
print(cca)
--
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