Files related to game.py

Author: PayalLakra

applications/brass-fanfare-with-timpani-and-winchimes-reverberated-146260.wav

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+from pylsl import StreamInlet, resolve_stream
+import numpy as np
+from scipy.signal import welch
+from scipy.integrate import simpson
+import time
+
+# Define the bandpower function
+def bandpower(data, sf, band, window_sec=None, relative=False):
+    band = np.asarray(band)
+    low, high = band
+
+    if window_sec is not None:
+        nperseg = window_sec * sf
+    else:
+        nperseg = (2 / low) * sf
+
+    freqs, psd = welch(data, sf, nperseg=nperseg)
+    freq_res = freqs[1] - freqs[0]       #Frequency resolution by simpson
+    idx_band = np.logical_and(freqs >= low, freqs <= high)
+    bp = simpson(psd[idx_band], dx=freq_res)
+
+    if relative:
+        bp /= simpson(psd, dx=freq_res)
+    return bp
+
+def normalize(value, min_value, max_value):
+    """Normalize a value to a range [0, 1] based on min and max values."""
+    return (value - min_value) / (max_value - min_value) if max_value > min_value else 0
+
+def eeg_data_thread(eeg_queue):
+    print("Looking for an LSL stream...")
+    streams = resolve_stream('name', 'BioAmpDataStream')
+    if not streams:
+        print("No LSL stream found!")
+        return
+
+    inlet = StreamInlet(streams[0])
+    sampling_frequency = 250
+    bands = {'Delta': [0.5, 4], 'Theta': [4, 8], 'Alpha': [8, 13], 'Beta': [13, 30], 'Gamma': [30, 40]}
+    buffer_length = sampling_frequency * 4
+    data_buffer = {'Channel1': [], 'Channel2': []}
+    powerData1 = []
+    powerData2 = []
+    c = 0
+    start_time = time.time()
+
+    baseline1 = baseline2 = 1  # Initialize baselines
+    min_power1 = min_power2 = 0
+    max_power1 = max_power2 = 1
+
+    while True:
+        sample, timestamp = inlet.pull_sample()
+        if len(sample) == 2:
+            data_buffer['Channel1'].append(sample[0])
+            data_buffer['Channel2'].append(sample[1])
+            if len(data_buffer['Channel1']) > buffer_length:
+                data_buffer['Channel1'].pop(0)
+                data_buffer['Channel2'].pop(0)
+
+            elapsed_time = time.time() - start_time
+            if len(data_buffer['Channel1']) >= buffer_length:
+                power_data = {'Channel1': {}, 'Channel2': {}}
+                for band_name, band_freqs in bands.items():
+                    power_data['Channel1'][band_name] = bandpower(np.array(data_buffer['Channel1']), sampling_frequency, band_freqs)
+                    power_data['Channel2'][band_name] = bandpower(np.array(data_buffer['Channel2']), sampling_frequency, band_freqs)
+
+                powerData1.append(power_data['Channel1']['Beta'] / power_data['Channel1']['Alpha'])
+                powerData2.append(power_data['Channel2']['Beta'] / power_data['Channel2']['Alpha'])
+                if elapsed_time >= 5:
+                    if c != 1:
+                        baseline1 = max(powerData1)
+                        baseline2 = max(powerData2)
+                        # min_power1 = min(powerData1)
+                        # max_power1 = baseline1
+                        # min_power2 = min(powerData2)
+                        # max_power2 = baseline2
+                        c = 1
+                        data_time = elapsed_time
+                        powerData1 = []
+                        powerData2 = []
+                    elif elapsed_time - data_time >= 0.25:
+                        current_power1 = (max(powerData1)-baseline1)/baseline1
+                        current_power2 = (max(powerData2)-baseline2)/baseline2
+                        # normalized_power1 = normalize(current_power1, min_power1, max_power1)
+                        # normalized_power2 = normalize(current_power2, min_power2, max_power2)
+                        eeg_queue.put((current_power1, current_power2))
+                        powerData1 = []
+                        powerData2 = []
+                        data_time = elapsed_time