docs: use PyQt5 in text, emphasize affordability, and update CMU affiliation

Author: Jshulgach

paper.md

@@ -12,7 +12,7 @@ authors:
     orcid: 0009-0004-0449-9918
     affiliation: 1
 affiliations:
-  - name: Neuromechatronics Lab, Carnegie Mellon University, Pittsburgh, PA, USA
+  - name: Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
     index: 1
 date: 26 January 2026
 bibliography: paper.bib
@@ -35,7 +35,7 @@ The Open Ephys GUI [@siegle2017open] is a widely used platform for neural data a
 
 The field of neural data analysis is supported by several specialized tools. The official `open-ephys-python-tools` [@OpenEphysPythonTools] provide basic file loading capabilities but lack high-level processing or real-time application layers. In the domain of myoelectric control, libraries like `LibEMG` [@Campbell2022] offer comprehensive pipelines but are often decoupled from the specific streaming protocols used by hardware like Open Ephys.
 
-`python-oephys` bridges these domains by specializing in the high-density spatial configurations typical of Open Ephys hardware while providing the real-time application layer (viewers and decoders) missing from low-level I/O libraries. It leverages the scientific Python stack, including NumPy [@harris2020array], SciPy [@virtanen2020scipy], and Matplotlib [@hunter2007matplotlib], to provide robust data structures and visualizations.
+`python-oephys` bridges these domains by specializing in the high-density spatial configurations typical of Open Ephys hardware while providing the real-time application layer (viewers and decoders) missing from low-level I/O libraries. It leverages the scientific Python stack, including NumPy [@harris2020array] and SciPy [@virtanen2020scipy], and relies on high-performance visualization frameworks like PyQt5 [@PyQt5] and pyqtgraph [@pyqtgraph] to provide the responsive interfaces necessary for real-time neural data monitoring.
 
 # Software Design
 
@@ -53,9 +53,9 @@ The field of neural data analysis is supported by several specialized tools. The
 `python-oephys` is a foundational component of the research infrastructure at the Neuromechatronics Lab at Carnegie Mellon University. Its deployment has significantly advanced several core research thrusts:
 
 1. **High-Density Myoelectric Control**: By providing a high-performance pipeline capable of processing 64+ channels of HD-EMG data in real-time, the toolkit enables the development of sophisticated human-machine interfaces. It supports the transition from laboratory-based offline analysis to live, closed-loop control of robotic prostheses and assistive devices.
-2. **Standardization and Reproducibility**: The modular design of `pyoephys` ensures that signal processing standards (e.g., CAR, specific filtering bands, and QC metrics) are consistent across various research projects. This reduces "re-invention" time and lowers the barrier for new researchers entering the field of neuro-engineering.
+2. **Standardization and Reproducibility**: The modular design of `pyoephys` also ensures that signal processing standards (e.g., CAR, specific filtering bands, and QC metrics) are consistent across various research projects. This reduces "re-invention" time and lowers the barrier for new researchers entering the field of neuroscience and physiological signal processing.
 3. **Low-Latency Inference**: The tight integration of PyTorch and ZeroMQ allows for sub-10ms feature extraction and classification latencies. This responsiveness is critical for minimizing the user-perceived delay in myoelectric control, which is a primary determinant of user acceptance and system efficacy.
-4. **Hardware Agnostic Processing**: While specialized for the Open Ephys ecosystem, the internal data structures and processing pipelines are extensible to other electrophysiology systems, providing a bridge between disparate hardware platforms and common machine learning frameworks.
+4. **Democratizing High-Density EMG**: Commercial HD-EMG acquisition systems are often prohibitively expensive for many research groups. By leveraging the open-source Open Ephys platform and providing a high-quality, free software toolkit, `python-oephys` makes high-density myoelectric research significantly more affordable and accessible to the broader scientific community.
 
 # Acknowledgements