Update README.md

README.md

@@ -208,7 +208,7 @@ Unlike EEG FMs that mix channels early, TinyMyo uses **per-channel patching**:
 * Patch length: **20 samples**
 * Patch stride: **20 samples**
 * Tokens/channel: **50**
-* Total seq length: **800 tokens** (16×50)
+* Total seq length: **800 tokens** (16 x 50)
 * Positional encoding: **RoPE (rotary)**
 
 This preserves electrode-specific structure while allowing attention to learn cross-channel relationships.
@@ -341,7 +341,7 @@ Pipeline:
 
 **WER:** **33.95 ± 0.97%**
 
-TinyMyo is EMG-only—unlike multimodal systems like MONA-LISA.
+TinyMyo is EMG-only, unlike multimodal systems like MONA-LISA.
 
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@@ -350,15 +350,15 @@ TinyMyo is EMG-only—unlike multimodal systems like MONA-LISA.
 TinyMyo runs efficiently on **GAP9 (RISC-V)** via:
 
 * **INT8 quantization**, including attention
-* Multi-level streaming (L3→L2→L1)
+* Multi-level streaming (L3 to L2 to L1)
 * Integer LayerNorm, GELU, softmax
 * Static memory arena via liveness analysis
 
 ### Runtime (DB5 pipeline)
 
-* **Inference time:** 0.785 s
-* **Energy:** 44.91 mJ
-* **Average power:** **57.18 mW**
+* **Inference time**: **0.785 s**
+* **Energy**: **44.91 mJ**
+* **Average power**: **57.18 mW**
 
 This is the **first EMG foundation model demonstrated on a microcontroller**.
 
@@ -381,7 +381,7 @@ This is the **first EMG foundation model demonstrated on a microcontroller**.
 * **Silent Speech Production:** 33.54% WER
 * **Silent Speech Recognition:** 33.95% WER
 
-TinyMyo matches or exceeds state-of-the-art performance—while being smaller and more efficient than all prior EMG foundation models.
+TinyMyo matches or exceeds state-of-the-art performance, while being smaller and more efficient than all prior EMG foundation models.
 
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