🧠 BrainFlux BrainFlux is a cutting-edge real-time neural data visualization tool built with C++ and ImGui, designed to help researchers and engineers intuitively explore and analyze complex brain activity data. Originally developed for HackDarmouth, BrainFlux focuses on rendering high-speed neuronal spike and brainwave data with responsive, interactive controls, making insights easier to uncover.

🚀 Inspiration Neural data — especially from interfaces like Neuralink’s N1 implant — can be overwhelming and complex. We wanted a powerful, lightweight visualizer that makes sense of this data in real time without sacrificing speed, flexibility, or clarity.

🧩 What It Does Visualizes real-time neural spike data and synaptic patterns

Provides smooth, interactive graphs using ImGui

Handles large-scale brainwave datasets with minimal latency

Offers an intuitive interface designed for researchers and engineers

🛠️ How We Built It Visualization: Developed a C++ application using ImGui for high-performance, real-time data rendering.

Data Preprocessing:

Noise Filtering: Used Python to remove static noise and amplify meaningful signals from raw brainwave audio data.

Lossy Compression: Applied lossy compression to reduce dataset size for faster processing and lightweight storage.

Noise Filtering (Python)

import os
import noisereduce as nr
from pydub import AudioSegment
import numpy as np

input_folder = r"C:\path\to\data"
output_folder = r"C:\path\to\filtered_data"

def process_audio(input_path, output_path):
    audio = AudioSegment.from_wav(input_path)
    samples = np.array(audio.get_array_of_samples())
    reduced_noise_samples = nr.reduce_noise(y=samples, sr=audio.frame_rate)
    reduced_noise_audio = AudioSegment(
        reduced_noise_samples.astype(np.int16).tobytes(),
        frame_rate=audio.frame_rate,
        sample_width=audio.sample_width,
        channels=audio.channels
    )
    amplified_audio = reduced_noise_audio + 20
    output_file_path = os.path.join(output_folder, os.path.basename(output_path))
    amplified_audio.export(output_file_path, format="wav")

for filename in os.listdir(input_folder):
    if filename.endswith(".wav"):
        process_audio(os.path.join(input_folder, filename), os.path.join(output_folder, filename))

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Lossy Compression (Python)

from pydub import AudioSegment
import os
import noisereduce as nr
import numpy as np

input_folder = r"C:\path\to\data"
output_folder = r"C:\path\to\2_filtered_data"

def process_audio(input_path, output_path):
    audio = AudioSegment.from_wav(input_path)
    samples = np.array(audio.get_array_of_samples())
    reduced_noise_samples = nr.reduce_noise(y=samples, sr=audio.frame_rate)
    reduced_noise_audio = AudioSegment(
        reduced_noise_samples.astype(np.int16).tobytes(),
        frame_rate=audio.frame_rate,
        sample_width=audio.sample_width,
        channels=audio.channels
    )
    amplified_audio = reduced_noise_audio + 20
    output_file_path = os.path.join(output_folder, os.path.splitext(os.path.basename(output_path))[0] + ".mp3")
    amplified_audio.export(output_file_path, format="mp3", bitrate="128k")

for filename in os.listdir(input_folder):
    if filename.endswith(".wav"):
        process_audio(os.path.join(input_folder, filename), os.path.join(output_folder, filename))

⚡ Challenges We Ran Into Managing real-time rendering while keeping performance smooth

Cleaning noisy spike recordings without losing important brain signals

Designing a UI that is both minimal and powerful for scientific use

🏆 Accomplishments Built a complete real-time brainwave visualizer from scratch

Successfully reduced noise and compressed large datasets efficiently

Designed a responsive ImGui-based user interface

📚 What We Learned Advanced noise reduction and audio processing with Python

Real-time rendering techniques with C++ and ImGui

Efficiently handling and visualizing complex biological datasets

🔮 What's Next for BrainFlux Integrating machine learning for brain pattern detection

Adding support for live neural data streaming

Exporting analytical reports and data summaries